diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index 57c0c337d..21e84c2fc 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -12,14 +12,18 @@ CPMAddPackage( "RUN_HAVE_STD_REGEX 0" # ) -if (benchmark_ADDED) - # patch google benchmark target - set_target_properties(benchmark PROPERTIES CXX_STANDARD 14) -endif() +CPMAddPackage( + NAME nvbench + GITHUB_REPOSITORY NVIDIA/nvbench + GIT_TAG main + GIT_SHALLOW TRUE +) ################################################################################################### -# - compiler function ----------------------------------------------------------------------------- +### compiler function ############################################################################# +################################################################################################### +################################################################################################### function(ConfigureBench BENCH_NAME BENCH_SRC) add_executable(${BENCH_NAME} "${BENCH_SRC}") set_target_properties(${BENCH_NAME} PROPERTIES @@ -35,6 +39,22 @@ function(ConfigureBench BENCH_NAME BENCH_SRC) CUDA::cudart) endfunction(ConfigureBench) +################################################################################################### +function(ConfigureNVBench BENCH_NAME BENCH_SRC) + add_executable(${BENCH_NAME} "${BENCH_SRC}") + set_target_properties(${BENCH_NAME} PROPERTIES + POSITION_INDEPENDENT_CODE ON + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/nvbenchmarks") + target_include_directories(${BENCH_NAME} PRIVATE + "${CMAKE_CURRENT_SOURCE_DIR}") + #"${NVBench_SOURCE_DIR}") + target_compile_options(${BENCH_NAME} PRIVATE --expt-extended-lambda --expt-relaxed-constexpr) + target_link_libraries(${BENCH_NAME} PRIVATE + nvbench::main + pthread + cuco) +endfunction(ConfigureNVBench) + ################################################################################################### ### test sources ################################################################################## ################################################################################################### @@ -47,6 +67,14 @@ ConfigureBench(DYNAMIC_MAP_BENCH "${DYNAMIC_MAP_BENCH_SRC}") set(STATIC_MAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_map_bench.cu") ConfigureBench(STATIC_MAP_BENCH "${STATIC_MAP_BENCH_SRC}") +################################################################################################### +set(STATIC_MULTIMAP_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap/static_multimap_bench.cu") +ConfigureNVBench(STATIC_MULTIMAP_BENCH "${STATIC_MULTIMAP_BENCH_SRC}") + +################################################################################################### +set(RETRIEVE_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/hash_table/static_multimap/retrieve_bench.cu") +ConfigureNVBench(RETRIEVE_BENCH "${RETRIEVE_SRC}") + ################################################################################################### set(RBK_BENCH_SRC "${CMAKE_CURRENT_SOURCE_DIR}/reduce_by_key/reduce_by_key.cu") ConfigureBench(RBK_BENCH "${RBK_BENCH_SRC}") diff --git a/benchmarks/analysis/notebooks/StaticMultimap.ipynb b/benchmarks/analysis/notebooks/StaticMultimap.ipynb new file mode 100644 index 000000000..0269c5690 --- /dev/null +++ b/benchmarks/analysis/notebooks/StaticMultimap.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install pandas\n", + "# !pip install matplotlib\n", + "\n", + "# Import libraries\n", + "import pandas as pd\n", + "\n", + "from Utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Specify the data path\n", + "datafile = '../data/static-multimap-data.csv'\n", + "\n", + "output_keys = ['Benchmark', 'Label', 'Distribution', 'MatchingRate', 'Multiplicity', \\\n", + " 'NumInputs', 'Occupancy', 'GPU Time (sec)', 'Elem/s (elem/sec)']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Read csv file\n", + "rawdf = pd.read_csv(datafile)\n", + "\n", + "# Filter out skipped tests\n", + "perfdf = rawdf[rawdf[\"Key\"] == rawdf[\"Value\"]].reset_index(drop=True)\n", + "\n", + "\n", + "# Add labels\n", + "perfdf['Label'] = perfdf[\"Key\"]\n", + "perfdf.loc[perfdf['Distribution'].notnull(), 'Label'] += \"_\" + perfdf['Distribution']\n", + "\n", + "# Trim data frame for visualization\n", + "perfdf = perfdf[output_keys]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualization Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "staic_multimap_insert_uniform_multiplicity\n", + "staic_multimap_insert_occupancy\n", + "staic_multimap_count_uniform_multiplicity\n", + "staic_multimap_count_occupancy\n", + "staic_multimap_retrieve_uniform_multiplicity\n", + "staic_multimap_retrieve_occupancy\n", + "staic_multimap_retrieve_matching_rate\n", + "staic_multimap_query_uniform_multiplicity\n", + "staic_multimap_query_occupancy\n", + "staic_multimap_query_matching_rate\n", + "staic_multimap_count_matching_rate\n" + ] + } + ], + "source": [ + "# Get benchmark list\n", + "unique_bms = perfdf[\"Benchmark\"].unique()\n", + "for it in unique_bms:\n", + " print(it)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `insert` performance by varying key muliplicities\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_insert_uniform_multiplicity\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `insert` performance by varying occupancies\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_insert_occupancy\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `count` performance by varying key muliplicities\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_count_uniform_multiplicity\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `count` performance by varying occupancies\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_count_occupancy\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `count` performance by varying matching rates\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_count_matching_rate\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"MatchingRate\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `retrieve` performance by varying key muliplicities\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
  • Fixed occupancy: 0.8
    • \n", + "
  • UNIFORM distribution
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_retrieve_uniform_multiplicity\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `retrieve` performance by varying occupancies\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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VKFmy5BX7nzt3jhUrVjB58uQrKgFly5alc+fOfPvtt+mKxQlTp8Jzz8FHH9n/yz/6yH52V0Vwy5YtxMTE0LVrV8C2EBYuXBiA2NhYxo4dy9tvv53mcbLr9e/WtRuNDzXmrRVvARC6LZRx7ceRN497UrpoJVAp5XGtW8Ps2bZb6Bdf2GWLFtmKoKs89yZJYVn6HvtlYF933mgppTxs716YPh2c/7ealfIpL9AC+MwY0xw4D1wxptAY84UxJtAYE1i2bNmrHnDMGOjYMfXXffdBZGTSfSIj7fLU9hkzJp3fxuXAgQM0bdqUqlWr8tRTT1GpUqUk66Ojo/n+++/p3r17qsfYvHkzLVq0SHX9hQsX+P333+nVqxeDBg1iajprMSNHjuS+++6jU6dOvP766wmtlHfccQezZ88mICCAxx9/nHXr1qW4f2hoKN27d6devXqUKlWKtWvXJln/9NNP8+677xLr1AB6Ur6Gn35q1736KhQrBi+/DF272vdixWDsWLv++PEr982IHTt2UKJECfr160fz5s0ZO3Zswu/i448/pnfv3lSsWDHN42Tn639wwUHeXfEuIVtDOHnhpNtaAUErgUopL3jxRXj6aXj2WfjqKzs+8O674fXXbf4FLwsDqib6XAU45O59M3KjpZRy2MKF9v2mm5yNI+vlU5gxZpXrczC2UugxUVEZW54ZVatWZcOGDezcuZNvv/2Wo0ePJln/0EMP0b59e2644YZ0He/EiRMEBARQr149xo+3wyp/+eUXOnXqROHChbntttsICQlJqGxICv9JxS/r1q0bu3fv5oEHHmDbtm00b96c8PBwqlSpwvbt23njjTfIkycPnTt35vfff7/iOFOnTmXgwIEADBw48IrKR82aNWnVqhU/Ot9CnaLt2+Gaa5Iuu+YaOJTev9g0xMTEsGzZMsaPH8/q1avZvXs333zzDYcOHSIoKIiHH344w8fMbtc/oEUAcRviOHzuML3r93ZbKyDoFBFKKS9o3Rq+/x569ICYGAgOtt2FOnVyJJzVQF0RqQkcBAYCd3phX6WULypZ0vZhBPD3T7rc+zJdxhhjjojIARGpb4zZDnQGtmQlmA8+uPr6GjVsF9DkqleHJUuycuYrVapUiUaNGrFs2TL69+8PwMsvv0x4eDiff/75Vfdt1KgRa9eupVmzZpQuXZr169czfvx4zp07B9ib8RUrVlCjRg3AVhQWL15Mly5dKF26NBEREZQpY1Pynzx5MuFngFKlSnHnnXdy55130qtXL/744w9uu+02ChQoQI8ePejRowfly5cnNDSUzp07J+x34sQJFi1axKZNmxARYmNjEZErujc+++yz9O/fP0nXRW+62nVs2NC2/iX+v3zxYoivm5Upk7W/gypVqtC8eXNq1aoFQN++fVm5ciUVKlRg586d1KlTB4DIyEjq1KnDzp07UzxOdr7+9frWg9FQuWllrq/i3mS/2hKolPKK+CeDv/1mu4Y6VAHEGBMDjALmYzPn/WSM2SwiI0RkBICIVBCRMOAx4HkRCROR4qnt68w3UUq5RXg4lCgB996bNDvoyZNeDyUr5ZPrEA8DU0RkAxAA/M+T8b7++pVd+gsXtsvdISwsjAsX7DxpERERrFixgvr16wMwadIk5s+fz9SpUxPGXqXmySef5PXXX2fr1svJUiNd/VjPnDnD8uXL2b9/P3v37mXv3r188sknCa0yHTt25PvvvwfsOLQffviBTq7/wBYtWpRwnLNnz7Jr1y6qVavG2rVrE7oGxsXFsWHDBqpXr54kpuDgYIYMGcK+ffvYu3cvBw4coGbNmixfvjzJdg0aNMDf359ffvkl479AD3vuOdv1d/FiiI627/fdZ5e7Q8uWLYmIiCB+SMWiRYvw9/fn5ptv5siRIwnXq3DhwqlWACF7X/9lkcu4puo1yA7BL49fpn+XKTLG5JjXtddea5RSvumOO4ypWNGYuLiM7QesMT5QvmTlpWWTUj7s+HFjBg40ZtasDO2mZZP1ww/GVK9ujIh9/+GHLB/SFClSxBhjzIIFC0yTJk1M06ZNTZMmTcznn3+esI2fn5+pVauWadasmWnWrJl5+eWXr3rMX375xQQGBpr69eubNm3amIEDB5rt27ebr7/+2gwYMCDJtidOnDBlypQxFy9eNKdOnTKDBg1KiGHs2LEmNjbWGGPM22+/bRo2bGiaNGliGjVqZMaPH2+MMWbu3LmmRYsWplGjRqZRo0Zm2LBh5sKFC8YYY6pXr27Cw8NNhw4dzNy5c5Ocd8KECWbEiBFmz549plGjRgnL169fb0TEfP3115n7hXrQjz8a06iRMXny2Pcff8z6MeOvvzGX/wYaN25s7rnnHhMVFXXV7VOTHa9/2Okww0uYhyY/lOHrn57ySex2OUNgYKBZs2aN02EopVLQowdUrmynhsgIEfnHGBPomai8Q8smpXIeLZuUUp507tI5ft7yM+2rt6dmyZoZ2jc95ZOOCVRKecXcuZAou7dSSvmGQ4fs5KVKKeVDiuYvyj0B93js+DomUCnlcfEdDtIYtqGUUt51+jRUqwbvvON0JMoNvv76awICApK8Ro4c6XRYykty0vU/dv4YH676MGGieE/QlkCllMd16gTNm8P77zsdiVJKJbJoEcTGwnXXOR2JcoNhw4YxbNgwp8NQDslJ1z9kawiPzHuEG2veSOnCpT1yDn0ur5TyqBMn4I8/bPI9pZTyKQsWQNGiWglUSvmU4K3B1C9dn0ZlG3nsHB6tBIpIdxHZLiI7ReTpFNY3EJG/RCRKRJ7IyL5Kqexh4ULbHbRbN6cjUUqpZBYsgBtvhHz5nI5EKaUAOB55nMV7FnNbw9tSnKzeXTxWCRQRP+AToAfgDwwSEf9km50ERgPjM7GvUiobmD/fzrncsqXTkSilVCK7dsHu3XDTTU5HopRSCWZum0msiaW/f3+PnseTLYGtgJ3GmN3GmEvANKBP4g2MMceMMauB6Izuq5TyfcbAvHn2HsvPzXOcKqVUlpQrBz/+CH309kIp5Tu2n9hOnVJ1CKgQ4NHzeLISWBk4kOhzmGuZp/dVSvmIqCh4+GEYOtTpSJRSKplixWDQIKhSxelIsq8KFUDkyleFClk6bNGiRQHYt28f1157LQEBATRq1IiJEycmbDN48GDq169P48aNuffee4mOTt6ekNS8efNo1aoVDRo0ICAggAEDBrB//34Ahg4dSs2aNRMySrZp0yZhv9DQUJo2bUqDBg1o0qQJoaGhCeuGDh1K4cKFOXv2bMKyRx55BBHh+PHjAPj5+SXJVvnmm29y6623EhAQQJ06dbjmmmsS1v3555907NiR+vXr06xZM1q2bMn69esTjl2jRo2E4x45coSBAwdSu3Zt/P396dmzJzt27MjcLzyzatRI+frXqJGlw8Zff4D9+/dz00030bBhQ/z9/dm7d2+SbR9++OEk26cmO13/maNmUujLQrRq1cqz1z+t2eQz+wJuByYl+nw38FEq274EPJHJfYcDa4A11apVM0qpnAVYYzxUTnnrde2117r/F6OUyrxLl4z54ANj9u/P9CG0bDLG2A4fKb+yoEiRIsYYY6KioszFixeNMcacPXvWVK9e3Rw8eNAYY8ycOXNMXFyciYuLMwMHDjSffvppqsfbuHGjqVOnjtmyZUvCspkzZ5qlS5caY4y55557TFBQ0BX7rV+/3tSuXdvs3r3bGGPM7t27Te3atc2///6bsF+TJk3M999/b4wxJjY21jRp0sRUrlzZhIeHJ/kuKVm8eLG5+eabkyzr0KGDWb16tTHGmK+++sp06dIlYV316tVNeHi4iYuLM9ddd5357LPPEtatW7fO/PHHH6meyyNSu85uuv7G2N/HggULjDH2b+D8+fMJ61avXm3uuuuuq/6Ojcmd1z895ZMnp4gIA6om+lwFOOTufY0xXwBfAAQGBpqMh6mU8pTly6FZM/vAXSmlfMaqVTBmDFStal8qZWPGQKKWiAzp2DHl5QEB8MEH6TpE/vz5E36OiooiLi4u4XPPnj0Tfm7VqhVhYWGpHuett97i2WefpWHDhgnLevfuneb5x48fz7PPPkvNmjUBqFmzJs888wzvvPMO33//PQCDBg1i+vTp3HXXXSxZsoS2bdsyd+7cdH2/tFx//fW8k8IclosXLyZfvnyMGDEiYVlAQIBbznmFlK7jHXfAQw+lvj7e8ePQP9m4tiVL0n3qLVu2EBMTQ9euXYGkLYSxsbGMHTuWH3/8kZCQkKseJztd/03HNvHq0leZGTjT49ffk91BVwN1RaSmiOQHBgKzvLCvUsoHnD1rk+69+qrTkSilVDILFkCePLaQUj7twIEDNG3alKpVq/LUU09RqVKlJOujo6P5/vvv6d69e6rH2Lx5My1atLjqecaOHZvQLW/w4MEJ+1177bVJtgsMDGTz5s0Jn+vWrUt4eDgRERFMnTqVgQMHJtn+woULSboDTp8+PV3fG2wXxr59+16xfNOmTVfElRPt2LGDEiVK0K9fP5o3b87YsWOJjY0F4OOPP6Z3795UrFgxzeNkl+t/+uJpTl44SbEC9sm5p6+/x1oCjTExIjIKmA/4AV8ZYzaLyAjX+okiUgHblbM4ECciYwB/Y8yZlPb1VKxKKfdbvBiio+Eq/y8rpZQz5s+H1q11AtO0pNVid7X09Rlo8bmaqlWrsmHDBg4dOkTfvn3p378/5cuXT1j/0EMP0b59e2644YZ0He/EiRN07tyZyMhIhg8fzhNP2BnK3nnnHfona7UyxlyRoj+lZf369WPatGmsWrWKzz//PMm6QoUKJRnXlR6DBw/m/PnzxMbGsnbt2gzt63ZpXceU1sf/fsqUydLfQUxMDMuWLWPdunVUq1aNAQMG8M0339CjRw+CgoJYkolj+/L1n71jNsYYlr2/jCpvVvH49ffoPIHGmF+NMfWMMbWNMa+7lk00xkx0/XzEGFPFGFPcGFPC9fOZ1PZVSmUf8+dDkSLQtq3TkSilVCInT8Lq1Tp5aTZTqVIlGjVqxLJlyxKWvfzyy4SHh/Pee+9ddd9GjRol3EyXLl2a9evXM3z4cM6dO5fmfmvWrEmybO3atfj7J521bODAgYwbN46uXbuSJ0/Wb62nTJnCnj17uPPOOxk5cmSKcf3zzz9ZPo+vq1KlCs2bN6dWrVrkzZuXvn37snbtWtatW8fOnTupU6cONWrUIDIykjp16qR6nOxy/YO3BJM/b36CpgV55fp7tBKolMqdjIG5c21PqwIFnI5GKaUSWbvWdgXV+QGzLlGLXLqWZ1BYWBgXLlwAICIighUrVlC/fn0AJk2axPz585k6dWqaN95PPvkkr7/+Olu3bk1YFhkZmeb5n3jiCd54442EjJR79+7lf//7H48//niS7apVq8brr7/OQ/Hj5NwgX758vPbaa6xcuTJJ3AA33ngjUVFRfPnllwnLVq9ezdKlS912/nSpXj3l7KDVq7vl8C1btiQiIoLw8HAAFi1ahL+/PzfffDNHjhxh79697N27l8KFC7Nz585Uj5Mdrv/ZqLPM2zmPsoXLkkfyeOX6ayVQKeV2u3bBnj36oF0p5YO6dLEJK1q1cjqS7O/IkZRzgx454pbDb926ldatW9OsWTM6dOjAE088QZMmTQAYMWIER48e5frrrycgIIBXXnkl1eM0adKECRMmMGTIEBo0aEDbtm3ZunUrd955Z8I2iceEBQQEcOnSJQICAnjrrbe45ZZbaNCgAbfccgtvv/12ikk4HnzwQWrXrn3F8uRjwp5++ul0f/9ChQrx+OOPM378+CTLRYSQkBAWLlxI7dq1adSoES+99NIV4yU9bu/elK9/smkcMsvPz4/x48fTuXNnmjRpgjGGBx54IMPHyQ7XP87E8XLHl6lQ9PL0Kp6+/mKziOYMgYGBJnmzrVLK+4yBTZugYkU7JCArROQfY0ygeyJzhpZNSuU8WjYppXxVesonbQlUSrmdCDRpkvUKoFJKudX27dCpE6xb53QkSikFQGR0JD9t/olzl64+RtHdtBKolHKrixdh+HA77EYppXzK/Pk2W6FmBc2Rvv766yRd7wICAlJMrKFypux6/ef+N5cBwQNYfXC1V8/rycnilVK50PLl8OWX0Ls3pDEtj1JKedf8+VC3Lrgmf1Y5y7Bhwxg2bJjTYSiHZNfrH7w1mLKFy3JD9fRNc+Iu2hKolHKrefMgf37o2NHpSJRSKpGoKNsKqFlBlVI+4kL0BX7Z8Qu3NriVvHm82zanlUCllFvNnw833ABFizodiVJKJfLnnxAZqZVApZTPWLBrAecunaO/f/+0N3YzrQQqpdwmLMxmBdWpIZRSPidPHujcWbspKKV8xtJ9SylVqBQda3T0+rm1EqiUcpv9+6FGDeje3elIlFIqmQ4d4LffoHhxpyNRSikA3r3pXTb+30by+eXz+rm1EqiUcps2bWD3bmjc2OlIlFIqkchIOHXK6ShynOafN0delitezT9vnqXjFnWNJ9i3bx/XXnstAQEBNGrUiIkTJyZsM3jwYOrXr0/jxo259957iY6OTvV4L7300hUTbteoUYPjx48DdvLtxx9/PGHd+PHjeemll67Yd+jQodSsWTMh8+SHH34IwOnTpxkyZAi1a9emdu3aDBkyhNOnTwOwd+9eChUqREBAAP7+/gwZMiQh1iVLliAiTJ48OeHc69atQ0SuiNcXtfuqXYrXv91X7bJ03KKJxpPs37+fm266iYYNG+Lv78/eZBPRP/zww0m2T4kvX/88efIw96e5Cef25vXXSqBSyi3i4uxLxL58mYh0F5HtIrJTRJ5OYb2IyIeu9RtEpEWidY+IyCYR2SwiY7wauFIqc2bOhNKlYcsWpyPJUa6vcj35/fInWZbfLz9tqrRxy/ErVqzIn3/+yfr161m1ahVvvvkmhw4dAmwlcNu2bWzcuJELFy4wadKkTJ+nQIECzJgxI6FScDXvvPMO69evZ/369YwePRqA++67j1q1arFr1y527dpFzZo1uf/++xP2qV27NuvXr2fjxo2EhYXx008/Jaxr0qQJ06dPT/g8bdo0mjVrlunv4k0tK7VkTOsxmBdNwmtM6zG0qtzKbecYMmQIY8eOZevWrfz999+UK1cuYd2aNWs45YaHO05d/4///pjSNUo7dv11igillFusXAl9+8Ls2dC6tdPRpE5E/IBPgK5AGLBaRGYZYxLfHfYA6rperYHPgNYi0hh4AGgFXALmicgcY8x/3vwOSqkMmj8fSpaE+vWdjiRbGTNvDOuPrE91fVRMFDFxMUmWxcTFsO7IOjp+0zHFfQIqBPBB9w/Sdf78+S9XMKOiooiLi0v43LNnz4SfW7VqRVhYWLqOmZK8efMyfPhw3n//fV5//fUM7btz507++eefJDfyL7zwAnXq1GHXrl34+fklLPfz86NVq1YcPHgwYVm1atU4c+YMR48epVy5csybNy/Jd3NaStfxjkZ38FDLhxjVehQNPm7AyrCVFMhbgKiYKNYcXsP4rrYV63jkcfr/lDThyZKhS9J97i1bthATE0PXrl2BpC2EsbGxjB07lh9//JGQkJCMf7FEnLj+l2IvMW/nPEqULsHFixcduf7aEqiUcov58+HECTsFl49rBew0xuw2xlwCpgF9km3TB/jOWCuBEiJSEWgIrDTGRBpjYoClwK3eDF4plUHGwIIF0KULJLohU1lXIG8Byhcpj2C7fwhChSIVrmgdzIoDBw7QtGlTqlatylNPPUWlSpWSrI+Ojub777+nexYHo48cOZIpU6YkdONLzdixYxO6A27cuJEtW7YQEBBwxc1+QEAAmzdvTrLvxYsXWbVq1RWx9u/fn6CgIP78809atGhBgQIFsvRdvKVi0YqUL1KeA2cOAHDgzAHKFynPNQWvccvxd+zYQYkSJejXrx/Nmzdn7NixxMbGAvDxxx/Tu3dvKlas6JZzefv6L96zmPOXzlOxaEXHrr+2BCql3GLePNsCWKqU05GkqTJwINHnMGxrX1rbVAY2Aa+LSGngAtATWOO5UJVSWbZ5Mxw+rFNDZEJ6WuwOnz1MrQ9rcTHmIgXzFuSfB/+hQtEKbouhatWqbNiwgUOHDtG3b1/69+9P+fLlE9Y/9NBDtG/fnhtuSH2ibUlljELi5cWLF2fIkCF8+OGHFCpUKNVjvfPOO/Tvf7l1a/fu3Ske3xiTsHzXrl0EBATw33//0b9/f5o2bZpk2zvuuIMBAwawbds2Bg0axJ9//pnq+b3tai13hfMVZvUDq2n0aSPe7/Y+Xb7vwuaHNidc/zKFy2So5S+5mJgYli1bxrp166hWrRoDBgzgm2++oUePHgQFBbFkSfqO7YvX/6NZH1EoXyHKFinr2PXXlkClVJYdPw6rV2ebqSFS+t/ApGcbY8xW4C1gITAP+BeISWFbRGS4iKwRkTXh4eFZiVcplRULFth3rQR6RMViFRkWMIw8kodhAcPcWgFMrFKlSjRq1Ihly5YlLHv55ZcJDw/nvffeu+q+pUuXJiIiIsmys2fPUqJEiSTLxowZw+TJkzl//ny642rUqBHr1q1L0lU1Li6Of//9l4YNGwKXx4Tt3LmTlStXMmvWrCTHqFChAvny5WPhwoV07tw53ef2BRWLVeSeZvfQ+bvO3NPsHrde/ypVqtC8eXNq1apF3rx56du3L2vXrmXdunXs3LmTOnXqUKNGDSIjI6lTp06qx/G16x8SGkLIthDaVG1DHsnj2PXXSqBSKssWLrQ9rrLJ1BBhQNVEn6sAh9K7jTFmsjGmhTGmPXASSHE8oDHmC2NMoDEmsGzZsm4LXimVQX37wldfQZUqTkeSY41rP4521doxrsM4tx43LCyMCxcuABAREcGKFSuo7xrXOWnSJObPn8/UqVPJk+fqt7Pt27dn1qxZnD17FoAZM2bQrFmzJF34AEqVKsUdd9yRJFtnWurUqUPz5s157bXXEpa99tprtGjR4oqKScWKFXnzzTd54403rjjOK6+8wltvvXVFTNnBk22fpFXlVjzZ9km3Hrdly5ZEREQQ/yB10aJF+Pv7c/PNN3PkyBH27t3L3r17KVy4MDt37kz1OL54/e9uejfdal9+cu7E9ddKoFIqyxo2hCeegMBApyNJl9VAXRGpKSL5gYHArGTbzAKGuLKEXgecNsYcBhCRcq73akA/YKr3QldKZVitWjBsmNNRpFs6shc3EJG/RCRKRJ5IYb2fiKwTkV+8E7FtDVo6dKnbWwG3bt1K69atadasGR06dOCJJ56gSZMmAIwYMYKjR49y/fXXExAQwCuvvJLqcZo2bcqoUaNo164dAQEBTJw4MdVsoo8//ni6skQmNnnyZHbs2EGdOnWoXbs2O3bsSLUi0bdvXyIjI5O0aAK0adOGvn37Zui8vqJisYrMu2ue26+/n58f48ePp3PnzjRp0gRjDA888ECGj+Nr1z/qYhT9ivSjZeWWCcuduP5iTPJeUNlXYGCgWbNGh+colZOIyD/GGLdWL0WkJ/AB4Ad8ZYx5XURGABhjJortyP8x0B2IBIYZY9a49l0GlAaigceMMb+ndT4tm5RyyMaNsGED9OsHVxnnkxkeKpv8gB0kyl4MDEqcvdj1IKo60BeIMMaMT3aMx4BAoLgxptfVzqdlk1LeFRsXy4oDK2hbtS1+eTzX6pee8klbApVSWXLwIPz1F7gSdmULxphfjTH1jDG1jTGvu5ZNNMZMdP1sjDEjXeubxFcAXetuMMb4G2OapacCqJRy0Hffwb332klMs4c0sxcbY44ZY1ZjH0QlISJVgJuBzE+ap5TymOX7l9Phmw6Ebgt1OhStBCqlsuaHH6BNGzh2zOlIlFIqmQULoF07KFLE6UjSK7XMxOn1AfAkkG1qve709ddfJ6Tvj3+NHDnS6bCUl2SH6x+8JZhCeQvRrY7zmfR0igilVJbMmwfNmoGbpupRSin3OHzYdgV9802nI8mI9GQvTnlHkV7AMWPMPyLS8SrbDQeGg52oPCcZNmwYw7LR+E/lXr5+/eNMHD9v/ZnudbpTNH/RtHfwMG0JVEpl2tmzsHx5tskKqpTKTRYutO/ZZO4al/RkL05NW6C3iOzFdiO9UUR+SL6RZi5Wyhl/HfiLw+cO09+/f9obe4FHK4HpyHAlIvKha/0GEWmRaN0jIrJJRDaLyBhPxqmUypzFiyEmJrvdYymlcoV166BcOUg2MbePS0/24hQZY54xxlQxxtRw7bfIGHOX50JVSmXErO2zyO+Xn171rpqvyWs8Vgl0Zbj6BOgB+AODRMQ/2WY9gLqu13DgM9e+jYEHsAOkmwG9RKSup2JVSmXOwoV2qE3btk5HopRSybz/PmzZAmnMIedLjDExwChgPrAV+MkYs1lERsRnMBaRCiISBjwGPC8iYSJS3LmolVLp8dqNr7HyvpUUL+Ab/1w9OSYwIcMVgIjEZ7jakmibPsB3xs5TsVJESohIRaAhsNIYE+nadylwK/C2B+NVSmXQ+PFw//2QP7/TkSilVApKl3Y6ggwzxvwK/Jps2cREPx/BdhO92jGWAEs8EJ5SKpPy+eWjecXmToeRwJOPx9KT4Sq1bTYB7UWktIgUBnqStI+8UsoHFChgk8IopZRP+ewzGDTI9ldXSimHvffXe7yw+AWnw0jCky2B6clwleI2xpitIvIWsBA4B/wLpFiS5+QsV0r5sp9+skNuXnkF8uVzOhqllEokOBiOH4e8mgRdKeUsYwwf/f0RDcs0dDqUJDzZEpieDFepbmOMmWyMaWGMaQ+cBP5L6SSa5UopZ3z7Lfz8s1YAlVI+5vx5m7b4ppucjkQppVh7eC17T+31mayg8TxZCUxPhqtZwBBXltDrgNPGmMMAIlLO9V4N6AdM9WCsSqkMuHjRZgbVqSGUUj5n6VK4dEkrgUopnxC8JRg/8aNP/T5Oh5KEx/pJGGNiRCQ+w5Uf8FV8hivX+onYgc89gZ1AJJB4hsefRaQ0EA2MNMZEeCpWpVTGLF8OFy7o1BBKKR+0YAEULAjt2jkdiVIqlzPGELw1mBtr3kjpwr6VqMqjneXTkeHKACNT2fcGT8amlMq8efNsRtCOHZ2ORCmlkqlWDYYNg0KFnI5EKZXLnY8+T2ClQHrU6eF0KFfQEdNKqUzp1cvOEaiUUj7lscecjkAppQAomr8oU2/zzRFtWglUSmXY+PFOR6CUUikID4cSJTRjlVLKJ+w6uYvapWo7HUaKPJkYRimVA1265HQESimViocfhqZNnY5CKaXYEr6FOh/V4YcNPzgdSoq0EqiUypDBgzXpnlLKB8XGwsKFcN11TkeilFIEbwlGEDrX7Ox0KCnSSqBSKt1iYuw9VrVqTkeilFLJrF0LJ0/qUyqllE8I3hJMu2rtqFisotOhpEgrgUqpdFu1Ck6f1qkhlFI+aMEC+96li7NxKKVyve3Ht7Px2EafmyA+Ma0EKqXSbf58yJNH77GUUj5owQJo0QLKlnU6EqVULvfz1p8B6Newn8ORpE6zgyql0m3ePDvcpmRJpyNRSqlkXn8dIiOdjkIppXio5UM0Ld+UKsWrOB1KqrQSqJRKt4cf1rkBlVI+ql07pyNQSikAShQsQa96vZwO46q0EqiUSre773Y6AqWUSkFIiO2i0LGj05EopXK5aZumcfjsYcZcNwYRcTqcVOmYQKVUuvz2G+zZ43QUSimVgiefhPHjnY5CKaV476/3+HHTjz5dAQStBCql0iEuDu66C557zulIlFIqmd27YedOnRpCKeW4faf2sfrQavo39N2soPG0EqiUStO//8LRozo1hFLKBy1caN+1EqiUclh8VlBfnhoinlYClVJpmj/fvueUeywR6S4i20Vkp4g8ncJ6EZEPXes3iEiLROseFZHNIrJJRKaKSEHvRq+USmLBAqhWDerXdzoSpVQuF7wlmOYVmlO7VG2nQ0mTVgKVUmmaNw8CAqBiRacjyToR8QM+AXoA/sAgEfFPtlkPoK7rNRz4zLVvZWA0EGiMaQz4AQO9FLpSKjljYN06+4TKx8ffKKVytkuxlyiYtyADGg1wOpR00eygSqmrioyEv/6CRx91OhK3aQXsNMbsBhCRaUAfYEuibfoA3xljDLBSREqISHwVOC9QSESigcLAIe+FrpRKQgT++w/OnnU6EqVULpffLz+L7lmEvXXIuirvVeHg2YNXLK9crDJhj4Vl+fjaEqiUuqrCheHgQRgzxulI3KYycCDR5zDXsjS3McYcBMYD+4HDwGljzAIPxqqUSoufH5Qo4XQUSqlc7myUfRjlrqygvev3Jr9f/iTL8vvlp0/9Pm45vlYClVJpKlMGKlRwOgq3Sal0Tv7YLsVtRKQktpWwJlAJKCIid6V4EpHhIrJGRNaEh4dnKWClVCoGD4YPPnA6CqVULnf47GHKvFOG7/79zm3HHNd+HHkkaVXNT/wY12GcW46vlUClVKqMgSFDYPZspyNxqzCgaqLPVbiyS2dq23QB9hhjwo0x0cAMoE1KJzHGfGGMCTTGBJYtW9ZtwSulXE6dgmnT4ORJpyNRSuVyIdtCuBR7icBKgW47ZsViFRkWMAw/8QMgX558DAsYRoWi7nkqr5VApVSqduyA77+HsKx3Pfclq4G6IlJTRPJjE7vMSrbNLGCIK0voddhun4ex3UCvE5HCYvt7dAa2ejN4pZTL77/bSUxzStpipVS2FbwlmIZlGuJfNnmeuawZ134ccSYOgLx58rqtFRC0EqiUuor4qSFy0vyAxpgYYBQwH1uB+8kYs1lERojICNdmvwK7gZ3Al8BDrn1XAcHAWmAjtgz9wrvfQCkF2KkhihWD1q2djkQplYuFnw9n6b6lHpkbMM7EYTAI4tZWQNDsoEqpq5g3D+rVg1q1nI7EvYwxv2IreomXTUz0swFGprLvi8CLHg1QKXV1xtinVDfeCPnyOR2NUioXC90WSpyJ80glMH7y+crFK7u1FRC0EqiUSsXFi7BkCdx/v9ORKKVUMhcvQvv22hVUKeW4bnW68WnPT2lSronbjx20JYgm5Zqw4f82uP3YHu0OKiLdRWS7iOwUkadTWC8i8qFr/QYRaZFo3aMisllENonIVBEp6MlYlVJJHTkCLVpAz55OR6KUUskUKgTffQd3pZicVymlvKbaNdX4v5b/57apIeIdOnuIFftXcLv/7W49bjyPVQJFxA/4BOgB+AODRCT5aMkeQF3XazjwmWvfysBoINAY0xjwwyZvUEp5SY0asHw5dO/udCRKKZXMoUO2S6hSSjlo0Z5FTN04ldi4WLcfe8bWGRgMtzfKZpVAoBWw0xiz2xhzCZiGnV8rsT7Ad8ZaCZQQkYqudXmBQiKSFyjMlSnclVIeFBnpdARKKZWC6GioXx+eecbpSDwiHb2oGojIXyISJSJPJFpeVUQWi8hWV0+qR7wbuVK5z/g/x/PsomevmM/PHYK2BNG4XGMalGng9mODZyuBlYEDiT6HuZaluY0x5iAwHpuO/TA2PfsCD8aqlErkwAEoUcJOwaWUUj7lr7/g3Dm47jqnI3G7dPaiOontLTU+2fIY4HFjTEPgOmBkCvsqpdwk4kIEv+3+jf4N+7u9K+jhs4dZtm8Z/Ru6P9lMPE9WAlP6bSTvu5HiNiJSEttKWBOoBBQRkRQ7/ovIcBFZIyJrwsPDsxSwUsqaP98+bG/i/jHOSimVNQsWgJ8fdOrknuMVLw4iV76KF3fP8TMmzV5UxphjxpjVQHSy5YeNMWtdP5/FToGT/OG7UspNZu+YTXRctEeygnq6Kyh4thIYBlRN9LkKV3bpTG2bLsAeY0y4MSYamAG0SekkxpgvjDGBxpjAsmXLui14pXKzefOgShXw12fISilfs2CBbQW85hr3HO/s2Ywt96z09KJKk4jUAJoDq9wTllIqueAtwVQtXpVWlVu5/9hbg/Ev6+/2yecT82QlcDVQV0Rqikh+bGKXWcm2mQUMcWUJvQ7b7fMwthvodSJSWGz7amfsEy2llIfFxMBvv9kJ4t3cu0EppbLmxAlYsyYnTw2Rnl5UVz+ASFHgZ2CMMeZMCuu1B5VSWRQbF8uBMwe4reFtbu8KevTcUf7Y94fHsoLG89g8gcaYGBEZBczHZvf8yhizWURGuNZPxE7W3BPYCUQCw1zrVolIMLAW28d9HfCFp2JVSl22ahWcPq1ZQZVSPqhQIZg+HQICnI7EU9LTiypVIpIPWwGcYoyZkdI2xpgvcN1TBQYGaopVpdIhJi6GVl+24tylcwnLjDHM3jGbsLNhBN0e5LZzzdg6w2OTzyfm0cnijTG/Yit6iZdNTPSzAUamsu+LwIuejE8pdaXq1eGtt6BzZ6cjUUqpZAoXhts9+3TcYQm9qICD2F5Ud6ZnR1fPqcnAVmPMe54LUancJ2+evJQtUpYedXpwT8A9xJk48kgenv39WWpcU8Ot5wraEkSDMg1oVLaRW4+bnEcni1dKZT9VqsCTT0LJkk5HopRSiRgDH30Eu3Y5HYnHGGNigPheVFuBn+J7UcX3pBKRCiISBjwGPC8iYSJSHGgL3A3cKCLrXa+eDn0VpXKcFzu8yJSNUyhTuAydvu3E3P/msmjPIsa2Heu2cxw7f4yl+5Zyu//tbu9mmpxHWwKVUtnLyZOwaJEdD1ismNPRKKVUIlu3wujRULAg1K7tvuMWK5ZyEhiHCsF09KI6gu0mmtxyUh5TqJRygzZV21C/TH0emPUAR84dYcneJdzX/D7KFSnntnOEbA0hzsR5fDwgaEugUiqRefNsT6tt2zxz/ClToEYNyJPHvk+Z4pnzKKVyoAWu6YK7dnXvceMLvNdes62N8a8zV+RUUUrlct3rdGfGthl0q92NJXuXuLUVEGxX0Hql69G4XGO3Hjcl6WoJFJFA4AbsnH0XgE3Ab8aYkx6MTSnlZfPmQZkycO217j/2lCkwfDhERtrP+/bZzwCDB2fumFo2KZWLLFgA9erZJ0juNHOmfb/1VrceVssnpXKWHSd28MrSVyiavyg7Tuzg/hb3u7UVMPx8OIv3LuaZds94vCsopNESKCJDRWQt8AxQCNgOHAPaAQtF5FsRqebxKJVSHhcXZyeJ79rVttS523PPXa4AxouMtMszSssmpXKZixdhyRLbV93dQkOhbl1o2NAth9PySamcafXB1RTMW5Bv+3zLiQsn3N4KGLot1GtdQSHtlsAiQFtjzIWUVopIAFAXO6+fUiobW78ejh3z3NQQ+1MpJVJbngYtm5TKTTZtguho988PeOqUHQj92GPunBhVyyelcqDBTQfTp0EfiuYvSve63Smcr7Bbjx+0JYi6perStHxTtx43NVetBBpjPklj/Xq3RqOUcsyyZfbdU3MwV6tmu4CmtDyjtGxSKpcJDLSZq/Lnd+9xf/0VYmKgb1+3HVLLJ6VylleWvkLzCs25pf4tFM1fFMDtFcDjkcdZtGcRT7Z90itdQSGdiWFcXRdKJPpcUkS+8lhUSimvGz3aZl6vUMEzx38xhVk/CxeG11/P/DG1bFIqFylWDAoUcO8xQ0Jsode6tXuPi5ZPSuUEUzZM4cUlLzJ351yPnid0WyixJtZrXUEh/dlBmxpjTsV/MMZEAM09EpFSyhEiUKuW545fqpR9L1fOnqt6dfjii8wnhXHRskmpnO7oUbjhBlixwr3HvXgR5s6FPn08MxBayyelsrXVB1dz/+z76VC9Ax90/8Cj5wreEkztkrUJqBDg0fMklt5SL4+IJEwdLSKl0DkGlcoxFiyAIUMgPNxz5wgOthXBsDCbhGbv3ixXAEHLJqVyvt9+g+XL7fyA7j7u+fNuzwqaiJZPSmVTh88epu/0vlQoWoGg24PI7+fmruiJnLxwkt/3/O6VCeITS29h9C7wp4gEAwa4A8hCJy6llC+ZMcMmyJs0yTPHj4qCWbPsHIT58rn10Fo2KZXTzZ9v565p7uZGtNBQKF4cOnVy73Ev0/JJqWzqhw0/cPriaf6870/KFinr0XOFbgslJi6G/v79PXqe5NJVCTTGfCcia4AbAQH6GWO2eDQypZRXGGPnB7zxRvfnXIi3cKGdd7m/m8s3LZuUyuGMsV0V3D13TWysfTJ1880eK/i0fFIq+3qizRP0a9iP2qVqe/xcQVuCqFmiJi0qtvD4uRLLSIlaCjhvjPkICBeRmh6KSSnlJYcPw1df2aydnpoaAmxX0BIlbEXTA7RsUiqn2rjRjgl0d9riP/+0/d/dmBU0FVo+KZWNfLXuKzYf24yIeKUCGHEhgt92/+b1rqCQ/uygLwJPYSc+BcgH/OCpoJRSnhUXZ7OBNmoEb71ll/38M5w96/5zXboEM2faey13P3DXskmpHO7SJTtBfNeu7j1uSIgtkHr0cO9xE9HySansZd7OeTww+wHe+fMdr51z5vaZxMTFcHsj72UFjZfelsBbgd7AeQBjzCGgmKeCUkp51kcfwbp1sHs3PP64fcheqZKdL9ndfv/dzsfs7q6gLlo2KZWTBQba/uqVK7vvmMbY8YBduthpJzxHyyelsontx7czMHggTco14ZOeV53q062CtgRRo0QNrq14rdfOGS+9lcBLxhiDHdiMiBTxXEhKKU+bNAnefNN20XzwQZt34d13Yfp0mzXdnYKCbO6FLl3ce1yXTJVNItJdRLaLyE4ReTqF9SIiH7rWbxCRFq7l9UVkfaLXGREZ484vpFSuV6qUnUcm+St+npms2rAB9uzxZFbQeHrvpFQ2cOriKXpP601+v/zMHDiTIvm980/11MVTLNy1kP4N+3u9KyikvxL4k4h8DpQQkQeA34AvPReWUsqTIiJsy19IiO1tBfb+yhi4cMF954mOtg/c+/Rx/xzPLhkum0TED/gE6AH4A4NExD/ZZj2Auq7XcOAzAGPMdmNMgDEmALgWiARC3Pd1lFJERFzOWCUCy5bZzxER7jl+aKg97i23uOd4qdN7J6WygTeXv8nuiN38fMfPVC9R3WvnnbV9FtFx0Y50BYX0ZwcdLyJdgTNAfeAFY8xCj0amlPKYzp3h6afhp5/g/fdhzBg7bq9ePds66C6LF9v7Ng91Bc1s2dQK2GmM2Q0gItOAPkDirH19gO9cT/FXikgJEalojDmcaJvOwC5jzD53fR+lVCILFthxey3cnDEvJATatoXy5d173GT03kmp7OHlji/TvU53bqh+g1fPG7QliGrXVKNlpZZePW+89CaGKQIsMsaMxT7FKiQi7p3tSynlNUOG2G6a5ctD1arw0kswfLjtEurOHglBQXbIjbsT+8XLZNlUGTiQ6HOYa1lGtxkITM1w0EqptO3da1MXd+4MhQu777h79sC//3ojK6jeOynl4xbsWsDJCycpkLcAHWt09Oq5T188zYJdCxzrCgrp7w76B1BARCpjuzMMA77xVFBKKc+5cAGeeMK2+A0YAN98AydP2h5XHTu67zwxMfaB+y23QMGC7jtuMpkpm1IqbU1GthGR/NiED0GpnkRkuIisEZE14eHhaYSklEpi4ECbxvijj9x73NBQ++6FSiB676SUz/r74N/0ntqbxxc87sj5Z22fxaXYS451BYV0dgcFxBgTKSL3AR8ZY94WkXWeDEwp5Rljx8L69TBnDvTs6bnzLF0KJ054riuoS2bKpjCgaqLPVYBDGdymB7DWGHM0tZMYY74AvgAIDAxMXslUSl3Nli0weTLUquXe44aGQpMmUNvz83+h905K+aRDZw/Rd1pfKhWrxDtdvTcdRGLBW4OpWrwqrSu3duT8kP6WQBGR64HBwBzXsvRWIJVSPmT4cPjgA89WAMF2BS1SxLOT0JO5smk1UFdEarpa9AYCs5JtMwsY4soSeh1wOtl4wEFoV1ClPKNkSTtp6R13JM0OWrJk1o4bHg7Ll3sjK2g8vXdSysdciL5A32l9OXvpLLMGzaJM4TJej+FM1Bnm75xPf3/nuoJC+gujR7CTnYYYYzaLSC1gsefCUkq525kzdqqGpk3ty5NiY2HGDOjVCwoV8uipMlw2GWNiRGQUMB/wA75y7TvCtX4i8CvQE9iJzQA6LH5/ESkMdAUe9MD3USr3OnjQdlE4ccK9g5PjzZ5tu5h6pyso6L2TUj7nuUXPsfrQakIHhNK4XGNHYpi9fTZRsVH09/dsV6m0pDc76B/Yvu3xn3cDo9PaT0S6AxOwN1qTjDFvJlsvrvU9sTdaQ40xa0WkPjA90aa1sFm1PkhPvEqppM6fhzZtbOvf2297/nx//GEfunu4K2imyyZjzK/Yil7iZRMT/WyAkansGwmUzmTISqmUxMTAnXfCP/9Ajx42Y5W7hYRA9eoQEOD+Y6cgs+WTUspznmr7FM0rNKdPgz6OxRC0JYjKxSpzXZXrHIsB0ugOKiJfiEiTVNYVEZF7RWRwKut1Li6lfMTIkXaITdeu3jlfcLBN6OepLqdZKZuUUj7o1Vft06PPPvNMBfDcOVi40LYCerj7lZZPSvmeTcc2ERMXQ/mi5bm72d2OxXE26izzds6jv39/8kh6R+V5RlotgZ8C41yF2SYgHCiIrbQVB74CpqSyr87FpZQP+Ppr+PZbeOEF71QC47uC9uzp3szuyWSlbFJK+ZJFi2wlcOhQuNtDN2fz5kFUlLfGA2r5pJQP2X58O+2+asfdTe/mo55uzjicQb/s+IWo2Chu93cuK2i8q1YCjTHrgTtEpCgQCFQELgBbjTHb0zh2SvNsJU+Bk9pcXIkrgToXl1KZtHEjPPQQ3HijrQR6w4oVcOSIZ7uCZrFsUkr5ishIuOsuqF8fPv7Yc+cJDYXSpe0k8R6m5ZNSvuPUxVP0ntab/H75eaLNE06HQ9CWICoVq8T1Va93OpR0jwk8ByzJ4LHdORfXM6meRGQ4tisp1apVy2CISuVsYWF2CMyUKeDn551zBgfbeQFvvtnz58pk2aSU8hWFC9upIKpUsemEPeHSJfjlF+jXD/J6Lzmnlk9KOSs2LpaBwQPZE7GHRfcsonqJ6o7Gc+7SOebunMsDLR5wvCsopH+KiMzw2lxcxphAY0xg2bJlsxiyUjlLjx6weTNUqOCd88XFwc8/2/MWLeqdcyqlsqnDrk4/PXrYufs8ZelSOH3am1lBlVI+4PlFzzN/13w+6fkJ7aq1czoc5uyYw8WYiz7RFRQ8WwnUubiUcsikSfDRR2CM91oAAf76Cw4d8nxWUKVUNrdiBdSsCTNnev5cISG2xdFbmbGUUj7hjkZ38HLHl3ng2gecDgWwXUErFq1I22qe75aeHhmqBIpIuvtqGGNigPi5uLYCP8XPxRU/Hxc2Rftu7FxcXwIPJTpX/FxcMzISo1K53bp1MGqUnW7LJO+A7WHBwVCggJ0f0JsyUjYppRx24gQMGmS7gHbq5NlzxcXZimb37h6ftDQ1Wj4p5V3h58MBaF6xOS908FJChDScv3SeX//7lX4N+/lEV1BIZyVQRNqIyBZsZQ4RaSYin6a1nzHmV2NMPWNMbWPM665lE+Pn4zLWSNf6JsaYNYn2jTTGlDbGnM7UN1MqFzp9Gm6/HcqUgR9+gDxeLGfi4mwlsFs3Oym9N2S2bFJKOcQYGDbMZo+aPt3zhcXq1bZ7gneygiah5ZNS3nfwzEGaTmzK/5b9z+lQkpjz3xwuxFzwma6gkM7EMMD7QDdc3TmNMf+KSHuPRaWUyjBj4L77YN8+OwSmTBnvnv/vv20imjfe8OpptWxSKjuZMAFmz7bv117r+fOFhtpkMN7IVHUlLZ+U8hBjDPeE3sOZqDMJy2LjYll+YDnnL52nd/3eDkZ3peAtwZQvUt4nxibGS3c7gTHmQLJFsW6ORSmVBatW2fn53ngD2rTx/vmDgyFfPrjlFu+eV8smpbKRuDg7aPjhh71zvpAQ6NgRSpb0zvmSyUz5JCLdRWS7iOwUkadTWN9ARP4SkSgReSIj+yqVk+w4sYNyRcoxNGAo9zS7hzOXznDq4imuq3Idjcs1djq8BJHRkcz5bw63NbwNvzxeTNSQhvRWAg+ISBvAiEh+V6Gz1YNxKaUy6LrrbEXw8ce9f25jbCXwppvgmmu8emotm5TKTh57DH76CSSlGaLcbNs22L7dyaygGS6fRMQP+ASbHd0fGCQi/sk2OwmMBsZnYl+lcgQR4aWOL7HiwApuqXcL/538jz/2/UHhfIX5vNfnToeXxK///UpkdCT9/X0ra156K4EjgJHYidzDgADXZ6WUw06ehD/+sD+3bOmde6vk1qyx3VBv935Xdy2blPJ1xsD//Z/NVgXeK6RCQux7nz7eOd+VMlM+tQJ2GmN2G2MuAdOAJF/AGHPMGLMaiM7ovkrlJN1qd6NY/mIEbwmmRokaXFvxWnrX603Dsg2dDi2JoC1BlCtSjvbVfas3eHoniz8ODPZwLEqpDDIGhg6FhQth714oX96ZOIKD7bCb3l7ugq9lk1LZwBdfwMSJUKOGd8fmhYbaJ2NVqnjvnIlksnyqDCTuQhoGtPbCvkplO+GR4dxY80Ze+eMV/rz3T0b9Oorvb/3e6bCSiIyOZM6OOdzd9G6f6goK6awEikhN4GGgRuJ9jDG+NepSqVzm3Xcv51hwqgJoDAQFQZcu3h92o2WTUj5uwwZ45BGbNnjsWO+d9+BBm63qf85lCMxk+ZRSM2l6J/tJ174iMhwYDlCtWrV0Hlop37Jw10LuDrmbc5fOUb90fXpN7UWXWl18rhVw3s55nI8+z+2NfCcraLz0ZgcNBSYDs4E4j0WjlEq3FSvg6afhttu8l2MhJevWwZ498Pzzjpw+FC2blPJN587BHXdAqVLw3XfenbMmfhJ658YDQubKpzCgaqLPVYBD7tzXGPMF8AVAYGCgl2eTVSprLsVeYtyicbz959v4l/Vnwd0LOHT2ED2n9GTzQ5udDu8KQVuCKFO4jM91BYX0VwIvGmM+9GgkSql0i4iAAQNs76rJk50ZBxgvKAj8/BwbdqNlk1K+6ocfYMcO+P13KFfOu+cOCYH69aGho60CmSmfVgN1Xa2IB4GBwJ1e2FcpnxcTF0PHbzryV9hfPHjtg7zX7T0K5ytMk3JN2DJyCw3KNHA6xCQuRF9g9vbZDG4ymLx50lvl8p70RjRBRF4EFgBR8QuNMWs9EpVS6qpKlIAxY6BzZ69n40wiPivojTdC6dKOhKBlk1K+6sEHoVUraNHCu+eNiIAlS5xJlZxUhssnY0yMiIwC5gN+wFfGmM0iMsK1fqKIVADWAMWBOBEZA/gbY86ktK+HvptSXpc3T14GNBrA49c/zm3+tyUsFxGfqwACzN8132e7gkL6K4FNgLuBG7ncpcG4PiulvOj8eShSBJ54Iu1tPW3DBti5E5580rEQtGxSytfs2GHf69XzfgUQbBbSmBi49VbvnzupTJVPxphfgV+TLZuY6Ocj2K6e6dpXqezsbNRZRs0dxR3+d3BzvZt55LpHnA4p3YK2BFG6UGk61ujodCgpSm8l8FaglivlsFLKIUuW2GkY5syxD9idFhRkh/k4OOxGyyalfMmFC3Yy+HPnbGUwrwNdoEJDoWJFmxnUWVo+KZUFaw6tYdDPg9gdsZuA8gHcjBezC2fRxZiLzN4+mwGNBvhkV1BI/zyB/wIlPBiHUioNR47AoEG226Wzw1ys+KygHTtC2bKOhaFlk1K+5NFHYeNG+PRTZyqAFy7A3Ln2yZQ3E9GkTMsnpTIhzsTxzop3uH7y9UTFRLF06FIevf5Rp8PKkPk753P20lmf7QoK6W8JLA9sE5HVJO3XrmnYlfKC2Fi48044fRoWLIBixZyOCDZvtg/6H3W2XNaySSlfMX06fP657R/evbszMfz2G0RGOp0VNJ6WT0plwpwdc3jytye5reFtfHnLl5Qs5OX5p9wgeGswpQqVolONTk6Hkqr0VgJf9GgUSqmreuUVWLwYvvoKmjRxOhorviuow8NutGxSyhfs3g0PPADXXQevveZcHCEhNltWx47OxXCZlk9KZcDRc0cpX7Q8ver1Yt7gedxU+ybEyfTnmRQVE8Ws7bO43f928vnlczqcVKWrEmiMWerpQJRSKTMG9u6FoUNh2DCno7ksOBjat3duknrQskkpn1Gxoq0Ejh4N+Ry66YmJgVmz4OabIX9+Z2JIRMsnpdInKiaKZ35/hklrJ7HuwXXULlWbbnW6OR1Wpi3YtYAzUWfo79/f6VCu6qqVQBFZboxpJyJnsRmtElYBxhhT3KPRKaUQgW++sfc3vmLLFvt66CFnzq9lk1I+5NIlKFQI3n3X2ThWrIATJxzvnqDlk1Lpt/34dgb9PIh1R9YxquUoKhev7HRIWRa0JYiSBUvSuWZnp0O5qrRGTY8FMMYUM8YUT/QqpoWYUp4VE2MfrG/daiuCTj1cT0lwsI2pXz/HQtCySSlfMHMmNGpku4M6LTQUChRwbjziZVo+KZUOX6/7mhZftGD/6f3MHDiTj3p+RMG8BZ0OK0viu4L2bdDXp7uCQtqVwE+8EoVSCoBDh2xiu61b4fnnYdIkWLfO6aiuFBwM7drZHmAOyVLZJCLdRWS7iOwUkadTWC8i8qFr/QYRaZFoXQkRCRaRbSKyVUSuz0osSmVb+/fbPurFi0Nlh5/eG2PHA3btCkWLOhuL3jsplS6rDq6ideXW/DviX3rXzxn5kn7b/Runo05zu7/vZgWNl9aYwOw3GlOpbCguDh57DL791k5t9c8/cPIk3HOPzQrqS7ZvtxngJ0xwNIxMl00i4oe9SesKhAGrRWSWMWZLos16AHVdr9bAZ653gAnAPGNMfxHJDxTObCxKZVvR0XbOmpgYmxW0QAFn4/n3X9i3D8aNczYOS++dlErFyrCVFPArQPOKzZnQfQJ58+TFL4+f02G5TdCWIEoULEHnWr7dFRTSrgTWFJFZqa3UNMdKucfEifD337BnD5w5A82b2/kAL/ngFMPBwfbdwa6gkLWyqRWw0xizG0BEpgF9gMSVwD7Ad8YYA6x0tf5VBM4D7YGhrvNcAnzwKinlYS+8AH/+CVOnQp06TkdjWwHz5IHePnFbovdOSiUTGxfL2yveZtzicdxY80YW3L2AAnkdfnjkZpdiLzFz+0z61O9Dfj/nk1OlJa1KYDjg8EhvpXK+yZNh/HgoUcJOsRUdDUuWQIcOcO6cL/Ruuiw4GNq0gSpVHA0jK2VTZeBAos9hXG7lu9o2lYEY17m/FpFmwD/AI8aY85mMRansJzraVgCHD4eBA52OxgoNhbZtoWxZpyMBvXdSudTPW37m1MVTVyzPmycv3234jkV7FjGg0QA+7/W594Pzgt93/86pi6eyRVdQSLsSeFZTHCvleRERl6da+PBDe2/VvLlNBnP+vO9UAnfuhPXr4b33nI4kS2VTSl21TDq3yQu0AB42xqwSkQnA08AVfdBEZDgwHKBatWqZDFUpH1CqlC2kktuwwU4O77Tdu20sPlAwuei9k8qVHl/wONWuqUbdUnUTlv198G+2Ht9KgbwF+Kr3VwwNGJot5/5Lj6AtQVxT4Bq61OridCjpklZimL3eCEKp3K5GDejSxbb6FSwIgYEwZ45tbStXzunoLovvCnrbbc7GQdbKpjCgaqLPVYBD6dwmDAgzxqxyLQ/GVgqvYIz5whgTaIwJLOsbrRNKZU5EhE28EhMDzz0HR4/az6dOOR2ZFRpq3/v0cTSMRPY6HYBSTnim3TMUzV+UyX0mM7nPZCb1nkSBvAXoVLMTa4evZVjzYTm2AhgdG03otlB61++dbbq5XrUSaIzJ0qgfzcCn1NVFRcGoUbB4sZ3e6sEHYcYMePFFuPdeeP99OxWDrwgKgtatwemGrSyWTauBuiJS05XYZSCQfPzOLGCIq4y6DjhtjDlsjDkCHBCR+q7tOpN0LKFSOVNcHDz9NLz+OixY4HQ0SYWEQNOmUKuW05EAWb93Uiq7GtZ8GJvDN/P5ms/pMaUHUzdOJSo2ivl3zad+mfppHyAb+33P70RcjMg2XUEh7e6gmaYZ+JS6uv374fbbbUKYxx6zr6++shlCa9SApUuhYUOno7xs925YuxbeecfpSLLGGBMjIqOA+YAf8JUxZrOIjHCtnwj8CvQEdgKRwLBEh3gYmOIql3YnW6dUztS3L8yeDSNGwODBTkdz2bFjdpL4F15wOhKlcr1NxzZRLH8xRswZQcWiFdl7ai+vdnqVPJJWx8PsL3hLMMULFOem2jc5HUq6eawSiGbgU+qqHnzQzgcYHHy5e6VvZDdP2c8/23cf6AqaZcaYX7EVvcTLJib62QAjU9l3PRDoyfiU8hnbt9v3uXPtgOVRo3yre8KsWbZrat++TkeiVK4VHRvN4BmDCdoSRMmCJSlRsARDmg5hzs459GuY8xvGo2OjCdkWkq26gkIalcDE3TNdDHDcGHMgpe2T0Qx8SiUTFwcXLkCRInZaiKgoqFfP6ajSJyjIjlWsWdPpSLJcNiml0it+POvSpTYtsK8JDbVdJ5o1czqSBFo+qdzi1MVTlChYgnx++SiUrxDj2o/j8esfZ9qmaYyYM4Kg24NyRSvg4r2LOXnhJP0b9nc6lAxJqyUwpRTHpVzdoAa5noinRjPwKZXI8eNw1112TuXQUKhe3emI0m/fPli9Gt580+lIEmSlbFJKXU1UlG31e+QRmxm0RAk7/UJyJUt6PbQkzp6FhQvhoYd8q3VSyyeVwx09d5T/Lfsfk9ZNYu3wtdQvU59v+36bsH5Y82GcvXQ2V7QCAgRtDqJY/mJ0q9PN6VAy5KqVQGNMp5SWi0gg8CG2y2ZqspKBz3BlBr4rEsu4YvwC+AIgMDAweSVTKZ/w9992/N+RI/DRR05Hk3HxXUH7+8hDriyWTUqp1CQerFyvns24mdL0EL5g3jy4dAluvdXpSJLQ8knlVBEXIhj/53g+WPUBUTFRDAsYRrECxa7YLr9ffp5o84QDEXpfTFwMIdtCuKX+LRTMW9DpcDIkU2MCjTFrRCStmcsSMvABB7EZ+O5Mts0sYJRrvGBrXBn4AETkgIjUN8ZsRzPwqWzKGPjsMxgzBipVsvkLArPhaLKgIDtvYe3aTkdydeksm5RSKfntNzv5+6VLNk2x70y5kLKQEChTJuVWSh+k5ZPKzi7GXMT/U3+OnDvCwMYDeaXjK9QtXTftHXO4JXuXcOLCiWyVFTRepiqBIlKeK7t2JqEZ+JSy02i98oqdA/CHH2zPquzmwAFYudJmhvd16SmblFIp+PJLm/mzYUNbAfT1wcqXLtnJVPv3Bz8/p6NJFy2fVHYTFRPFLzt+4Tb/2yiYtyCv3/g611a8lmYVfGcMrtOCNgdRNH9RutXOXl1BIe3EMB9xZYFVCmgDPJLWwTUDn8qt9uyxc+mVLAl//WXH/+XJpmOjfa0rKGS9bFJKJdOmDQwdChMmQNFs0Fi1eDGcOeOTWUG1fFLZXUxcDD9s+IGXlrzEvtP7WHX/KlpVbsW9ze91OjSfEhMXw4xtM+hVrxeF8hVyOpwMS6slcE2yzwY4ATxmjDnmmZCUyt6CguxE708/Dc895xvZNLMiONjOw+xjDQNaNimVVRs22H/gL78MjRrB5MlOR5R+oaE2zXKXLk5HkhItn1S2FGfimLF1BuMWj2Pb8W0EVgrky1u+pGWllk6H5pP+2PcHxyOPZ8uuoJB2YphvRaQ5UBvYbIzZ6p2wlMp+oqPhySfhgw/guutgyBCnI8q6gwftOMZXXnE6kqS0bFIqi374AYYPt5k/R46E8uWdjij94uJg5kzo3h0K+d7Tdy2fVHYVGR3JQ3MeomyRssy4YwZ9G/RFfCvzrk8J2hxEkXxF6FGnh9OhZMpVO6iJyDhgOnAbMEdEHvBKVEplMwcPQseOtgL48MN2Sq2qVdPay/fNmGHfb/exh1xaNimVSVFRdkqFu++GVq1g7drsVQEEm7n08GGfywoaT8snlZ0s37+ce0LvISYuhqL5i7J06FI2jNjArQ1v1QrgVcTGxTJj2wxurndztuwKCml3Bx0IBBhjIkWkNDAP+NLzYSmVvRw8CNu2wdSpNrleThEcbHuJNWjgdCRX0LJJqYwyxmb8nD8fxo6F//0P8mYqP5yzQkJs3D17Oh1JarR8Uj5h8IzBHD139IrlVYpX4ZHWj/DcoueYu3MuFYpWYOfJnTQo04CGZRs6EGn288e+Pzh2/li27QoKaVcCLxpjIgGMMSdEJJumtlDK/eLiYNEiOySlVSvYuxeKXTldTrZ15AgsWwYvvOB0JCnSskmpjBKxXT8feABuu83paDLHGFsJ7NTJ+cnqU6flk/IJpy6eouo1VRncZHDCsi/Xfslvu3/j23+/pWTBkrzV5S1GtRpF4XyFHYw0e6jyXhUOnj2YZNntQbdTuVhlwh4LcyiqzEurElhbRGa5fpZknzHG9PZYZEr5sIgIO+bvl1/gzz/h+utzVgUQbFdQY3yvK6iLlk1KpYcx8PbbULiw7at+yy1OR5Q1W7fCf//ZyVd9l5ZPyie82OFFbvvpNibePBGDwRjD3TPupmyRstzb/F4ev/5xril4jdNhZhu96/dm8rrJXIq9lLAsv19++tT38TlVU5FWJTD5txrvqUCUyi7WrrXTJYSFwUcf2SQwOVFwsO0G6u/vdCQp0rJJqbScPm2nfQgNhbvushXC7D7GJzTUvvv2RPZaPimfEFgpkErFKtH2q7YcPneYh1s9TJtqbQi6PYg82kCdYePaj+Pr9V8nWeYnfozrMM6hiLImreygS70ViFK+5sgR+OQTm4OgWjWbS2H9evi//4OyZeGPP3JuBfDYMZvc5tlnffOeUcsmpdKwcaPt8rlnj81YNXq0b/5jzqiQENv/vnJlpyNJlZZPymnh58P5cu2XTF43md0RuxGE4dcO54OVHzD/rvlaAcykisUqcmuDW5m6aSoA+fLkY1jAMCoUreBwZJmTVnbQPiIyMtHnVSKy2/XyoamjlXKvAwegZUs4cQIeeQTq1rXZyP/9F9q3t62BObUCCPY+Ky7OZ7uCatmk1NWEh9vJ38+etZOqP/JIzqgAHjgAa9b4bFbQeFo+KSfExMUQcSECgP2n9/Pcoueodk01pvSbwk21b+Kfw//QtlpbmlVo5nCk2deJyBOsDFuZ8DlvnrzZthUQ0qgEAk8CsxJ9LgC0BDoC/+ehmJRy3P/+Z3tPffqpnSS9fn2YNg3mzIFff7UtgTlZcLCt+DZp4nQkqdKySankjLHvZcvawmvtWmjXztmY3GnmTPvet6+jYaRDlsonEekuIttFZKeIPJ3CehGRD13rN4hIi0TrHhWRzSKySUSmikjBrH8d5cv2ntrLuEXjqPFBDR5f8DgALSq2YPfo3Sy+ZzF3NrmTVzq9wppDa3ihvW9messOLsVe4rafbuPQ2UPc2uBW8kiebN0KCGlXAvMbYw4k+rzcGHPCGLMfKOLBuJRy1JIl0K8fvPUWtGgBo0bZlr9z5+DQIaej86zjx23jQf/+Pt14oGWTUomFhdluCr/9Zj/ffTdUrOhsTO4WGmoHKvvgnDXJZLp8EhE/4BOgB+APDBKR5COzewB1Xa/hwGeufSsDo4FAY0xjwA87XYXKgebsmEO3H7pRa0ItXl/2Ok3LN6Vfw34AiAg1S9ZM2LZV5VbsG7NPWwEzyRjD8NnDWbpvKV/3+ZpPen5CjRI1snUrIKSdGCZJ/mVjzKhEH3N4W4jKreLiIDYWbr7Z9qrq1Qs+/hiioyEyEooXdzpCzwoNtd/fV7uCumjZpHKvUqVsiuKUnDvn3Vi85eRJ+3Ru7FinI0mPrJRPrYCdxpjdACIyDZtoZkuibfoA3xljDLBSREqISHyNPy9QSESigcJADn9smbv8d+I/apeqTR7Jw8LdC9kavpUXO7zIsObDqHZNtavum9Z6lbo3l7/Jt/9+y0sdXmJQk0EA7Bq9y+Gosi6tlsBVIvJA8oUi8iDwt2dCUspZmzbZDOTnztlukbNn2wfqY8faimGJEk5H6FnBwVCrFgQEOB3JVWnZpHKviAjb9TMuznZXyJPncuuY73eVzJw5c+zTKR8fD+iSlfKpMpC4FTHMtSzNbYwxB7GZSPcDh4HTxpgFGYxd+ZjI6Ei+//d72n/dnnof12PpXpt36NVOr7LnkT282PFFreB5UPCWYJ5d9Cx3NrmTFzrkrO60abUEPgqEisidwFrXsmux/dv7ejAupbxq+3b4/XebAbRpU5sZc8ECuP9+m1jvv/9sopgffnA6Us86edL+Hh5/3Ke7goKWTUrBwoXw1FO22X7y5JzdTSEkBCpVgsBApyNJj6yUTymVvCY924hISWwrYU3gFBAkIncZY5L8zyUiw7HdSKlWTSsPvurUxVM8v+h5ftjwA6ejTlOnVB3e7Pwmjcs1BqBYgRw2ObEP+vvg39wdcjdtqrZhcu/JiI/fGGVUWlNEHAPaiMiNQCPX4jnGmEUej0wpLzh2DF5+GT7/3E72PmgQlCxph9a0bw+PPmpbBqtUgdq1nY7W82bOhJgYOx7Ql2nZpHKtEycu/9ylC0ydCgMG+PxTmyyJjIR582DYMNvq6eOyWD6FAVUTfa7ClV06U9umC7DHGBMOICIzgDZAkkqgMeYL4AuAwMDA5BVM5UH3hN7DwTMHr1hevmh5pvSbwpmoM/x34j+urXQtRfIV4Zcdv3BzvZt5oMUDdKjeIcdVQnzZ/tP76T21NxWLViR0QCgF8+a8HEtptQQC4Cq49OZK5RgXLtgWvjfesPcXDz4IL75oK4CJlS4NHTo4EqIjgoKgRg249lqnI0kfLZtUrhEdbTN+vvyy/RwZCYULw8BckPdj4UJbaGezrq6ZLJ9WA3VFpCZwEJvY5c5k28wCRrnGC7bGdvs8LCL7getEpDBwAegMrMnKd1DudTbqLBWLVeSeZvckLPthww/8d+I/7pt5H9M3T6dUoVLseWQP+fzy8d/D/5HPL5+DEedOZ6PO0uvHXlyIucCiexZRtkjOTDXg+4/UlPKA48fhlVegUyfb0vfJJ1CunNNROSsiwiYW9PGsoErlLsbAL7/Y+VrGjLncHbJwYUfD8qrQULjmGujY0elIPM4YEwOMAuYDW4GfjDGbRWSEiIxwbfYrsBvYCXwJPOTadxUQjO2CuhF7j/eFd7+BupoXOrzA77t/p23VtnSp1YXzl84zZeMU/gz7k+mbpzOw8UCCbg9KmMxdK4DeFxMXw8CfB7IlfAvBtwfjXzZ5ct6cI10tgUrlBEuWwIwZMGECVK0K27ZB9epOR+U7Zs+2jQ2+3hVUqVxl0ya45RY7Wekvv0DPnraLQkpPapJ3ZcgJYmJs4dSrF+TLHTfExphfsRW9xMsmJvrZACOT7+da9yLwokcDVJlWsWhFyhctz6t/vMr/Ov+PBbsWUDRfUd7u+jYDGw/UcX4+4PH5j/Prf78y8eaJdK3d1elwPEorgSrH27YNnnzS3kdUrQrPPGOzfWoFMKmgIPv7adXK6UiUyuXCw22GpoEDbQvgL7/ATTddrgSdPOlsfN60fLkdB5k9soIqdYVdJ3cRsi2E0G2h/HngTwyGXSd38WTbJ5m5fSYL7l5Ay8otnQ5TAZ+u/pQP//6QR697lAcDH3Q6HI/T7qAqxzp1CkaOhMaNbSvgG2/YLKA5bf5kdzh92mZDzS1dQUWku4hsF5GdIvJ0CutFRD50rd8gIi0SrdsrIhtFZL2I6Hgb5T5RUTB+PNSpY5OghIfb5TffnGtawa4QEgIFCkC3bk5HolS6GGMIP2//7V6IvkCTz5owduFYzkef58UOL/LviH/pXLMzXb/vSkCFAK0A+oh5O+cxeu5obql3C+90fcfpcLxCWwJVjpU3r239GzECXnhBx/xdzS+/wKVLuaMrqIj4AZ8AXbFZ9laLyCxjTOLJmHsAdV2v1sBnrvd4nYwxx70UssrpjLHj3saOhV27bKVv/HgomzOTEaSpeHE4ezbpsmLF7OvMGWdiUuoqYuJiWLZvGaHbQgndHkrZwmVZM3wNhfIVYlr/aTQt35QaJWokbP9ixxdp/nlz/r5fp7X1BZuObeKOoDtoUr4JP972I355/JwOySu0EqhyjLg4mDIFvv0W5s6FokVtV9DclD8hs4KCoHJluO46pyPxilbATmPMbgBXhr0+QOJKYB/gO9fYm5UiUkJEKhpjDns/XJXjHTpku37WrQvz59uun7lZ8gpgWsuVctAHKz/g1T9e5eSFkxTMW5Cbat/ErQ1uxRiDiNC7fu8r9gmoEMC+Mft0kncfcPTcUXr92Iui+Ysye9BsiuYv6nRIXqPdQVW2EhkJ69bBkSNJly9ZYidzHzLEdgONX68VwLSdPWun4LrttmwxBZc7VAYOJPoc5lqW3m0MsEBE/nFNuqxUxh05YuepAfsEZulSWL9eK4BK+bATkSf4dv233Dr9Vg6dtdM3litSjp51e/LzHT9zfOxxZg6cydCAoWnO6acVQOddiL5A3+l9OXb+GLMHzaZK8SpOh+RVHm0JFJHuwATAD5hkjHkz2Xpxre8JRAJDjTFrXev2AmeBWCDGGBPoyViV75swwU7rULkyhIXZe6W334ZRo2y3z2rVbEvgwIG5pjKTZVOm2KzzUVEwfbpNCjN4sNNReVxK/zMnnzD5atu0NcYcEpFywEIR2WaM+eOKk9gK4nCAatX0P3vlcvEivP8+/O9/9h9ez55Qr16uaYZXype0/LIlW8O3XrG8XJFy7H5kNwARFyL4fsP3hG4L5Y99fxBrYqlcrDK7Tu6iUrFK3NnkTu5sknwqR+Xr4kwcw2YOY1XYKn6+42eurZRNJkh2I4/dKicad9MD8AcGiUjyyTYSj7sZjh13k1gnY0yAVgBVSIidy2/VKtiwAfbsgYIFbabPM2fgrbds0pc779QKYHpNmQLDh9s5EwGOHrWfp0xxNi4vCAOqJvpcBTiU3m2MMfHvx4AQbPfSKxhjvjDGBBpjAsvm1rFd6jJj7JOWBg3g2WehSxfYssVWAJVSjuhQvQODGg/iyBNHEl4jW46kWflmrDu8DoDI6EgemfcIR88f5el2T7P6gdUcePQAN1S/weHoVVa8tOQlpm+ezptd3uTWhrkz+7AnWwJ13I1ym88+g9dft9NjvfYaTJpke081a2bzKJQu7XSE2c9zz9nutYlFRtrlObw1cDVQV0RqAgeBgUDyx7izgFGucqs1cNoYc1hEigB5jDFnXT/fBLzixdhVdnXuHDz8MFSqBF9/DZ06OR2Rb7pwwekIVC4yts1YGn7SkOfbP8+BMweYvmk6n675lDgTR3RcNL/c+QuVi1fW8Xs5zA8bfuDVP17l3oB7GdtmrNPhOMaTlcCUxtS0Tsc2lYHDXB53Y4DPjTFfeDBW5eP27rUP0YcOtRWVPn1s9s9SpeyUWVoJzLh9+1Jevn+/d+PwNmNMjIiMAuZju6p/ZYzZLCIjXOsnYidq7gnsxHZVH+bavTwQ4hrrkRf40Rgzz8tfQWUXYWHw8cf2yVWxYvDHHzb5i1/uyDyXYXFxcPfdqa8vphNpK/eIM3HsPbWXWiVrcW/ze2n7VVsOnj1IHslDxaIVeaHDC0kSumgFMOdYvn859826j041OvFZr8/SHLuZk3myEqjjbpRb7NkD//0Hu3fDPffA6NG2BfCff+yQmho1nI4w+4mLs0lzkrcEgh1bmdMZY37FVvQSL5uY6GcDjExhv91AM48HqLKXUqUgIiLldQUKQN++dsxfgwZeDSvbeeop+PlnePddeOwxp6NROczuiN38vvt3ft/zO4v2LOJM1BkinopgbJuxTFwzkfdueo/Xlr3G0qFLqV2qttPhKg/YdXIXfaf1pUaJGgTfEUx+v/xOh+QoT46e0nE3KlPOnoWPPrLj/QBq1rRTZpUube+1zp6FL7+091Vvvpl751DOiieftBXA5L+7woVtt1ulVAZERNgxf7Gx8MMPUCVRhrmtWzXpS3p89pkt6EeOhEcfdToalQMcPXeUC9G2e/GElROo/WFthv8ynD/2/UG3Ot347ObPMBjKFy3PiMARvLniTfrU76MVwBwq4kIEvab2wmCYc+ccShUq5XRIjvNkS6COu1EZsnu3rfx99ZVN9tK+vb2n8vODxx+H22+3SfXGjrX3WN9/Dx07Oh119vP++/ZB+6hR0Lo1PP+87QJarZqtAObw8YBKeU5cHLzxBpQvDz/+aAuxmjWdjsr3/fqrLZBuvtlOm5GLu2f5mvtn3c+mY5uuWF4kfxHm3zWfvHl8Z7rp0xdP88e+P/h9j23t23RsEzMHzqR3/d50q9ONj/J8ROeanWlQpsEVXQDHthnLT5t/4rkbnnMoeuVJ0bHR9A/qz66Tu/htyG/UKVXH6ZB8gsf+9eq4G5URX38N991nK3x33AGPPGKnK0isWjVbgVGZ99NPtpdVv372XsvPD+66y+molMqmzpyx3RIATp+Ga66BBQugQgVNU5xe69bZQr9ZM5g2zQ72Vj6jgF8BKharyFNtn0pY9vOWn1l1cBV+4v6xrS8sfoHdEbuvWF68QHE+6flJksrbxZiLnI06S9kiZdl1chf1P65PrImlYN6CtKvWjsFNBtOkXBMAGpRpQIMyqXfHLl+0PPvG7MvV48NyKmMMI38dyaI9i/imzze0r97e6ZB8hkdLWx13o1ITGWmnIvD3h7Zt4cYbbVbK//s/mzxPud/SpTbnQrt2tsea5qZQKpMOHbITl37+ua38AYSH20qgFmDpFxYGvXrZfv6//AJFizodkUrm6XZP02xiMz7v9TmlC5UmzsRxT+g9fHazZxJqbA7fTFRMFAMbD0xY9vue31l1cBWxcbGsPbKW33f/zqK9i1i+fzl3Nr6TyX0mU6tkLV7q+BJtq7bl+qrXUzBvwQyfWyuAOdN7f73Hl2u/5Nl2z3JPwD1Oh+NTxNbDcobAwECzZs0ap8NQV3HggJ3v78svbVbPkSNt8jzlWZs22cpfpUqwfLm958ouROSf7D5XqJZNOUhYGNSuDTEx0L8/PPGE7baQ0v+lIikvV7YV9YYbbOavFSugSROnI8qw3FI2jZwzkiL5i1CrZC0emvMQIkLpQqUpnK8wRfIX4dc7f6V6ierM2DqDHzf+SJH8RSict3DC+rFtxlKsQDHWHV7H9hPb7fJ8RRLWNy7XmDySh6iYKDYe3cgt025h1+hdFMpbiENnD3HzjzfzaqdXeXHJi6w7Yufua1KuCZ1rdqZ3/d50qqnTraiUhW4Lpd/0ftzmfxvT+08nj+SeHhrpKZ+034XymkcesRVAY2xSl0cesfcAyrMOHIDu3aFIEZg3L3tVAJVynDGwZAn8+y+MGWMHJI8fb8ev1apltylZMuVxbCVLejPS7CM62nYB3bzZjgfMhhXA3CS+NXBKvymULFSSzjU7U7pQaSJjIomMjqRwvsIAnLxwkq3Ht3L+0nkioyM5H23fH73OJvqZvnk6b61464rjRz0fRX6//Dy+4HE+Wf0JglD67dIUzFuQOBNH7ZK16VWvFxEXI8jvl59ONTpRvmh5r/4OVPaz9vBaBs8YTGClQL7t+22uqgCml7YEqiwxBk6csNM3FSiQdF1UlM32ffvtNgvlxx/buelGjtRpHbzl1CnbAnjgACxbBk2bOh1RxuWWp+3Kx8TE2ALsnXfsfDSVK8POnVAw493MVCLGwIMP2u4gX34J99/vdESZlpvKppFzRrJs/zJKFCzB0qFL0911Mv4eU0Q4eeEkR88dTVJBPH/pPLf53wbA/J3zWX1oNXsi9jBt8zQGNR7E/J3z+bDHh9za8NbMf0mV6xw8c5BWk1qRN09eVt2/igpFKzgdktdpS6DyqJAQePppOHbM/r8+dCi89ZbNlj5xon0dPQqFCsGtt9rkb8p7Ll60La47dtgWwOxYAVTKEcuWwZAhsHevndx94kT7WSuAWff227by98wz2boCmNs83e5pJn00ibmD52Zo7FzibUsVKnXVtPzd6nSjW51uAJyKOsX+0/spV7QcfRv0zXTcKvc5d+kct0y9hTNRZ1hx74pcWQFML60EqkxZvhweeshmQe/YEQ4fhgcesL169u61vX169rRdPrt2dTra3Ccuzt6zLl1qr9GNNzodkVI+7uhRO06tbl2bijg+HXHv3prp011++sk+ORwwAF57zeloVAZUvaYqYY+GUbaId+ZjfqH9CwR8HsCsgbM0YYtKt9i4WO6acRf/Hv2X2YNm07S8Pv2+Gq0EqkyZMAFeeslm9tywwWb3/v57KFcO7r3XzuVXt67TUeZOxthpIIKCbE+2QYOcjkgpH7Z9u50487vvoFMnmDsXqle3T1CU+/z5p30y1bYtfPONVqyzIW9VAAGaVWjGmgfW0KJiC6+dU2U/Vd6rwsGzB69Yfk2Ba+hZt6cDEWUvWgqrTNm2zf6fXq0adOgA587ZhCN168Lo0VoBdNJ779lK+iOPwOOPOx2NUj5q1SrbX7phQ1sBHDrUTp6p3G/nTtuiWrUqhIZqt1qVLtdWulZbAdVV9a7fm/x++ZMsyyN5GNxksEMRZS9aCVQZ8s8/NtPkpk225a91a9vdsFAh2w302DGoWdPpKHOvqVNtxvrbb7eVQf3/M5soVcperOQvTeXqXrGx9gWwaJEd+/f887B/vx33V7++s/HlRCdO2LEBYDOBlinjbDxKqRxjXPtxCElvdPLnyc+4DuMciih70UqgStO+fbaCB7ar4aZNNsNnyZK2FdDfH37/3T7oHTvWTkWgvG/RIrjnHmjf3jZsaG+rbCQiwv7jSv6KiHA6suwptUp1/vy2nzTYLgv798Mrr9h+7Mr94rNT7d8PM2dqFxGllFsdOXeE4gWKJ3zOlycf9za/V5PBpFOuuk08fRrefBO6dYM774TFi52OyHfFxMDs2XYqrJo17X0SQGCgrRB+/LEdMrNqlZ2C4PnnbdfDp55yNOxca8MGm4G1Xj3tbZXtnTzpdATZX3ylOjwcXn4ZyrrGMsXFXW6JKlJEn1h5UlwcDBtms4h9+60dC6iUUm5w/tJ5nljwBC2/bAnYyh9A3jx5tRUwA3JNJfD06ctJTB5+2E5SPnQofPqp05H5nvfftxW/3r1h3Tp47jl48cXL6/O60gk1bgzTp0NYGPz9t22F0u6H3rd/P/ToYedqnDtX56fO1o4fh4AA20oVHe10NNlfjx628GrVyk74DtCli6Mh5RrjxsG0afDGGzYbqFJKucGv//1Ko08b8e5f73Jf8/vYPmo797e4nzySh2EBw7QVMANyTXbQiRPt9AU//nh5Wdeu9t7g7rvtDXRuFRtrW0U7d7aVuP37bRfPDz+EXr3sRO/KN508acdonj9vhzdVrep0RCpLSpS4PKBz40ano8lezp+3g2K///7ysnfftS1//v7OxZUbffUV/O9/dh5A7R6ilHKDI+eOMGbeGKZvnk7DMg1ZNmwZ7aq1A+zYwPm75msrYAblmpbAJUtsF1Cw3RkHDLBjpho0gLVrnYzMOYcP26maatWyFeL4B+Xvvgvz59vuhVoB9F0XL0KfPrBrl+0C2qSJ0xGpLMub9/J0BX/9ZZetX+9oSD5v+3YYMwYqV7aTlSYeR9m+vVYAvW3hQnjwQbjpJtvVRruHKKWyIM7E8cU/X9Dwk4aEbAvhlY6vsO7BdQkVQICKxSqya/QubQXMoFzTElimjG3hAntPNWeOHadeoAAULuxoaF539Kid6H3mTNsKeOONMH785SEbmlDE98XGwl132eE206dDx45OR6SypGTJlG+WRWx3xpkzvR9TdvDff/ZJXr580L+/LdjattVCzCmbNtnr0LChTcCjTxGVUlmwJXwLD/7yIMv3L6djjY583utz6pWu53RYOUau+Z/y/vvhrbfsPUPfvrBli80GfuaMbfGaNs3pCLPuwAE7lKh5czsUZtasy+uOHoUVK+zPpUrBjh3w6KP2/fffbQ+0/PlTPq7yLcbYho+ff7bjN++4w+mIVJadPJlydtDDh23XOoBTpy5Pb5BbHT4Mr756uYth3bowaZIt/H780WapErlcqU7+0gGznnPokJ0KokgR+5S1ePG091FKqRRcjLnIC4tfIGBiAFvCt/BV769YNGSRVgDdLNe0BHboAM8+C9ddZ+8bDh2y46dCQuw9xdKlMHCg01FmXlgYXH89DB4MX3xhuwg+9hgsWGAT5IWEQMWKsGePfTi7YYP20smu3nnHZmd97DFbGVQ5WPny9j0mxg7QLV7cVnZKlHA0LK8yBv74w3YtnDHD/i769LHLReC++67cRzOsete5c3DLLfb3roOTlVJZsHjPYkbMGcGOEzu4q+ldvHfTe5QtUtbpsHKkXFMJBBgxwiaBWbsWSpe+PFTkllsgKsr+/Ndf8M03dqxc2Wz0N/fBB7ZF6K237OdDh2yjwSef2Ja/UaNg+PDLvaS0Apg9/fCDbQQZMMBWBlUukTevLbxGjbLZrEJDc89YtzfftE/wSpaERx6xBXmdOk5HpeLFxsKgQXacxaxZtiuKUkpl0InIE4xdOJav139NrZK1WHDXArrW7up0WDlarukOGq9IETs9ROL7Jz+/y+MC//nH9r6qVw8++sg+dM4O/vzTJngJC7Of8+WDKlXsA9nZs22ywQYNnI1RZc3ChXbarY4d7bRbOuwpl3nwQZvG9/Rp26Uhp44T3LjRju2Lz1Q1YAB8/TUcPGgHL2sF0HfE903/5Rf7H+bNNzsdUY4gIt1FZLuI7BSRp1NYLyLyoWv9BhFpkWhdCREJFpFtIrJVRK73bvRKZYwxhh82/ECDTxrw/Ybvebrt02z8v41aAfQCvY1MZtQo+PdfaNnSjq8LCPDtSeVPn7atfZs32/kPP/vMLu/Z02b4PH8eatRwNETlBuvWQb9+Nt9CSIhNaKRyoXbtYM0a+5Rq7Fi4dMnpiNzj0iU7MLt9e2ja1Fb6Nm+262rVspO6FirkaIgqBRMmXO6b/tBDTkeTI4iIH/AJ0APwBwaJSPJm/x5AXddrOPBZonUTgHnGmAZAM2Crx4NWKpN2ndxFtx+6cXfI3dQpVYe1w9fyRpc3KJwvl2VsdIhWAlPg728rUKGhcOECrF7tdERXMsZWWCtXtu8VKtjeUr162fXnztlKbKdOUKmSs7GqrNm711bqS5a0k8HnpuFgKgVVq9pxV/Pn22xOUVH2H7wvK1Uq5UQtpUrZwuy662yXwkOHbGtfWBiMHOl01OpqQkNt5a9fP+2b7l6tgJ3GmN3GmEvANKBPsm36AN8ZayVQQkQqikhxoD0wGcAYc8kYc8qLsSuVLtGx0by5/E0af9aYVQdX8UnPT1g+bDlNyutcV96klcBUiNjcA5s3X06+ERwML7xgW9ecEBlpu3bGx3fhgu0ptXq1nSrrgw/gttts4puqVW2c8YkFVfZ04oSdDP7iRVsBrFzZ6YiUTyhUCGrWtD+PHg1t2sDu3c7GdDUREZcznsbG2grssGF2uYgd6Dpvnk1X/PjjdtC28l1//20n3m3ZEr7/Xvumu1dl4ECiz2GuZenZphYQDnwtIutEZJKIFPFksEpl1MqwlVz7xbU88/sz9Kzbky0PbeGhlg/hl8fP6dByHS2501Cw4OWpE/76y2YSbdDAzs1mjHdi2Lr18lzIvXvDtm12+eTJ9hUYaD8PGQL79tmx+Tt2wNSpmqU7O7twwV7vvXvtNW3UyOmIco6sjLlxrfdz3WT94r2oU3H77bblLDAQfvvN6WhSd/SobTGqXx+6dbPjyOINGGCXaWXCNxUvnrQFt3VrW0Bt3pz7Jtr1vJTStiW/20htm7xAC+AzY0xz4DyQUvk2XETWiMia8PDwrMarVLqcvniakXNG0mZyGyIuRjBz4Ex+vuNnKhfXp9tOyVXZQbPq3XftnIKjR9vpJD791I6Fb9rUM+f77z+b0XPJEpvo5bbbbGK8+vVT3ydfPjtuTGVvsbH2Qftff8FPP9lkRso9Eo256Yp9gr5aRGYZY7Yk2izxmJvW2DE3rROtfwQ71sb5xyxduthxgn362IrUO+/YSUB9LQVwlSo201bbtvDKK7YbYcGCTkel0uPs2ZSXO9UtJmcLAxLPsVEFOJTObQwQZoxZ5VoeTAqVQGPMF8AXAIGBgV56nK1ygyrvVeHg2YNXLC9VsBQF8xXk8NnDjG49mlc7vUqxAsUciFAl5tHHrjnqabtLu3a2++Xnn9uHoNu3u/f4e/fanjYA5crB8ePwxht2LuSpU+18h752b6fcZ8oUqF7dzggQGmpnBejf3+mocpxMj7kBEJEqwM3AJG8GfVW1atknBrfeCi+9ZDNpOmnnTjutwxtvXF42bpzt1rB8uR3/p9mNlErJaqCuiNQUkfzAQGBWsm1mAUNc91DXAaeNMYeNMUeAAyIS/6i4M7AFpbykd/3e5PfLn2SZIJy8eJJyRcqx6v5VfND9A60A+giPtQTmuKftifj52Ra6AQMud7f86CNbORsxwt7AZ0RsrB3v9dln9r1FC/tg/5prdFL33GTKFPt3FRl5eVlwMNx0Ewwe7FxcOVBK42lap2ObysBh4APgScC3/hcrWhSCgmxf8CpVbH/1kye9N74uMhJ+/tn2UV+61HbtvPvuy+tfeME7cSj3On3a6QhyFWNMjIiMAuYDfsBXxpjNIjLCtX4i8CvQE9gJRALDEh3iYWCKqwK5O9k6pTxqXPtxfL3+6yTLDIYX2r/AuA7jyJtHOyD6Ek+2BOa8p+3JXHONraAZA7//bqdoaN486ZQSO3bApEl2Sq+Usrl//719iH/LLXYS++efhxkzLq/XCmDu8eSTSSuAYD8/95wz8eRgmR5zIyK9gGPGmH/SPIkT425ELvcX/+wzm+p4+XLvnHvMGDswOSwMXn8d9u+Hb76x60qWTDk7aMmS3olNZZwxdtoOnWDW64wxvxpj6hljahtjXnctm+iqAOK6ZxrpWt/EGLMm0b7rjTGBxpimxpi+xpgIp76Hyn0K5C1As/LNEj4Lwl1N7uLlTi9rBdAHebISmJUMV3D5aXuch+JzGxE7d9uMGTZT+4032i58Q4fasVzLlsH779usnZs2waJF9gE9QFycXR4cbO+ZXnkFqlVz9OsoLzt1ymZaP5R81IfL/v1eDSc3yMqYm7ZAbxHZi32wdaOI/JDSSYwxX7huxgLLli3rrtjTr1Mn+6Tqxhtt/3V3OnnSdn8ICLCTWAI88oh9ArZjh+0KmjiV7cmTl7ODJn7FF4TKt+zcadMSDxpkW5WVUuoq/jvxH6N+HUXV96uy6uAq8oitXhTwK8A7N+kUMr7Kk5XAnPu0PcU47HCcLVtsBtFffrFDdHbuhG+/tRXENm3g2muhc2fbAgj2wflvv9mkL/nyORa+ckBMjG2sqVPHTu9RtGjK2+lDAbfLypibZ4wxVYwxNVz7LTLG3OXV6NOrYUM7wLhLF9tP/cEHsza5fFycLawGDbKTj44ebfvGx89R2KgRdOyoGT6zs6goeO01aNzY/gf20UewciUUS6Xnc2rLlVI5njGGP/b9Qd9pfan/cX2+XPslAxoN4N8R//LgtQ+SR/Jwb/N7qVC0gtOhqlR4sm02K0/b+2OftvcECgLFReSHlG62fC3LVaFCtkvn8uVwzz32/8hRo2yX0Kgomwvhtdfs2C/Q7p651cKFNoHj5s022c8HH9ifk48JLFzY9qxT7uOGMTfZR4kSdnLR55+Ht9+2KWc7dMjYMeILrgsX7JOufPnggQfgvvtsS6DKGZYsgf/7PzsH0e2320KpUiW77swZJyNTSvmQ6NhogrYE8d5f7/HP4X8oXag0z7d/nodaPpRQ4RvXfhzzd81nXIdxDkerrsaTlcCEp+3AQexT8zuTbTMLGCUi07CJGU4bYw4Dz7heiEhH4Amffdqeirg4KFLE3j/t2WO7hv7f/8Fdd9lEH4UKOR2hcsKOHfDEE/a+vFYt20Lct699GBB/P/3cc7YLaLVqtgKoSWHczxjzK7ail3jZxEQ/G2BkGsdYAizxQHju5edns3Tec8/l8V3Hj0OZMqnvc+mSnZxy8mQ4fNh2+SxSxA5+btpUp3bIScLDbaH03XdQsyb8+iv06OF0VEopHxNxIYIv137Jh6s+5ODZg9QvXZ/Pe33OXU3vonC+pPOFVixWkV2jdzkUqUovj1UCc9XT9hTccgtMnAi9esGcOXbZ0qUQEWGTx6jc5dQpO97zo4/sA4C33rJDqJJnyR88WCt9ykPiK4BLltjxgikpXhzuv99WCI4ft+PBhg6F6GjInx9atfJWtMrT4uLg669tRqqzZ+04zuee08nflVJJ7Dy5kwkrJ/D1+q85H32ezjU788UtX9C9TveEsX8qe/Joqp5c9bQ9mQcesMli2ra1PWv27oUff7RjATM6hYTKvmJi4Msv7RRpJ0/aHnSvvQblyzsdmcq1Gje+/POjj9o/Tj8/WwEUsU8qeve2f6w33WTXqZxl82Y7TnT5cpu9bOJEm0lWKaWw4/2W71/OeyvfY+a2meTNk5c7m9zJo9c9SrMKzdI+gMoWtDriIQULwoIFdmqIpUuhbFn45x87EbjKHVIa96dDqJTj4ruBjh5t0xZPmGC7iz75pF1+8KAtsFTOExlpM5eNH28zx371lW3p1cHpSinseL/gLcG8t/I91hxaQ6lCpXj2hmcZ2XIkFYtVdDo85WZaCfSgvHlt1s/bbnM6EuVN27fbITa//HLluD+lfMaECXD99bBqFXTtenm5VgBzpjlzbJayvXth2DCbKOhq40KVUrnGqYun+PKfL/nw7w8JOxNGvdL1+OzmzxjSbMgV4/1UzqGVQKXcJCLCPmRPa9yfUj5j4ED7UjnXwYO2IPr5ZzttyNKl0L6901EppXzA7ojdTFg5gcnrJnM++jydanTis5s/o2fdnjreLxfQSqBSWRQTA198AS+8YMf93X+/rQzquD+llGNiYuCTT+z0IDExNtXwE0/YBD9KqVyhyntVOHj24BXLyxQqQ/sa7QndFkoeycOgxoN49LpHaV5RMxfmJloJVCoLEo/769jRDrHScX/K55UsmXL/5JIlvR+Lcr/Vq23il7VroVs3WxmsXdvpqJRSXta7fm8mr5vMpdhLCcsE4fiF4yzes5in2j7FyJYjqVy8soNRKqdoW69SmbB9u50G5Kab7BzaM2bAokVaAVTZxMmTYMyVr5MnnY5MZcXp0/Dww9C6tZ3fcfp0mDtXK4BK5VLP3/B8isvf6PwGBx49wP86/08rgLmYtgQqlQHJx/29/bZNsqjj/pRSXlO8uJ3bL7n41t2RI+1cNNdc4924lFKOM8bw98G/Cd4STPDW4CStgHnz5OWB5g/wdLunHYxQ+QqtBCp1FVOm2PmT9+2DUqXg0iU4f17H/SmlHJRSBRBsa+7q1RAY6N14lFKOijNxrAxbaSt+W4I5cOYA+fLko0utLoxuNZpnfn+GqNgo8uXJxwsdX3A6XOUjtBKoVCqmTIEHHrDdPcH2lMuTxz5gf/ZZZ2NTSqkUaQVQqVwhzsSxYv8KgrcE8/PWnzl49iD5/fJzU+2beO3G17il3i2ULGTHef938j8+/+dzhgUMo0LRCg5HrnyFVgKVSkF4uB1aE18BjBcXZzOBaiVQKeV1sbHw669OR6GUckhsXCzL9y8naEsQM7bO4PC5wxTwK0D3Ot150/9Nbql3C9cUvLIb+Lj245i/az7jOoxzIGrlq7QSqJRLXJxN7vLllxASAtHRKW+3f79341JK5XKHD8PkyfYJ1IEDTkejlPKimLgY/tj3B0Gbg5ixbQbHzh+jYN6C9Kzbk/4N+9OrXi+KFSh21WNULFaRXaN3eSlilV1oJVDleocPw9df23us3bvt2L+HHoKffrLrkqtWzfsxKqVymfinUhMnwsyZdq6/rl3hgw/gttucjk4p5UHRsdEs2buEoC1BhGwL4XjkcQrnK8zNdW+mv39/etbtSdH8RZ0OU2VzWglUuVJsLMybZ1v9fvnFfu7Y0SZ76dcPChaEli1h+HCIjLy8X+HCds5lpZTyiBMn4Jtv4PPP4b//oHRpOxnp8OFQp47dplixlJPDFLt6a4BSynmpTeBeqWglJveZTPCWYEK2hXDywkmK5CtCr3q9uN3/drrX6U6R/EUciFjlVFoJVLnKvn3w1Vf2FRYG5crBE0/AffdB3bpJtx082L4/95ztAlqtmq0Axi9XSim3MAb++gs++wyCgiAqCtq1gxf/v717j7Kyrvc4/v7OMDAgEOiAATODQIpcFQXkMpnKpBKCmroySBPwGHa01tGyVidOnsrSVrXCylyCaR0pbZ2SvKWleYkG5VYKIx7FCzCIF9AYAbnMzPf88dububDnArP3fvbl81rrWTP72c/ez/c3G78+3/37Pb/ft0KvX3Fx8+Nra6OJU0Q6LdEC7gVWwI4PdzB96XR6de3FzOEzuWTkJZwz7By6F3WPMFrJZSoCJecdOAAPPhh6/R57LOw75xxYtCgs+F5U1Ppr58xR0SciKVJbC/fcE4Z8rlsXevKuvBK+8AUYMybq6EQkBRacuoAla5c029fgDcwcPpPLxl7G2cPOprhLcSuvFkkeFYGSszZuhCVLwsiqt9+G0lJYuBDmzYPBg6OOTkTy1j/+EXr9fvObsPDoKaeEb6kuvRR66j4fkVyya/8untn0DI+/9jiPv/Y4695Z1+z5LgVdmH/yfG6feXtEEUq+UhEoOWXv3jCz5+LF8OSTUFgI550X1vs799zwWEQk7fbsgfvuC71+K1dC9+7w2c/CggXhBmQRyQkH6g+w6s1VB4u+FTUrqGuoo1thNyrKK/j+tO9z8rEnc8F9FxxcwP3GM2+MOmzJQyoCJSe8+GIo/H7967Co+5Ah4f69K66AgQOjjk5Eclrv3q1P1PLcc6Hw+9WvYOdOGDECbr0VLrsM+vRJe6giklzuzobtGw4WfU+98RQf7P8AwzhlwClcP/l6KodWMrVsarP7++aNm6cF3CVSKgIlqyxd2jhRS2kpTJ8O69dDVVW4t+/CC0Ov31lnQUFB1NGKSF5IVADG948cGZLTxReHXr+PfxzM0hufiCTV1tqtPPH6EwcLv227wnpSw/oOY/aY2VQOreTM487kmB7HtPoeWsBdoqYiULLG0qWhwPvww/B4y5awdvKAAfDDH8Lll0O/ftHGKCLSzC23hCEJ/ftHHYmIHKGde3fy9KanDxZ9G7ZvAKCkRwnThkyjcmgl04ZMY0jfIR1+Ty3gLlFTESgZb+/ecH/fggWNBWBTRUVw/fXpj0tE8lxDA6xa1fYxN9yQnlhE5Ii0tm5fSfcSrp5wNY+/9jgrt66k3uvp3qU7pw8+nXnj5lE5tJKxx46lwDTsSLKTikDJSG+9BQ8/HJZ2+Mtfmi/Y3tKWLemLS0Ty3P794VupZcvgj3+EbduijkhEOmHW8FksWbuEAw0Hmu3f/uF2bvrbTUwYOIGvV3ydyqGVTC6dTLcu3SKKVCS5VARKRnCH55+Hhx4Khd/KlWF/WVkYSTVzZlg6a/PmQ19bXp7WUEUk39TWwqOPhsLv4YfD4x49wk3JF1wQJnkRkayxt24vq99czfLNy3llxyuHFICFVsjimYu5cMSF9CnuE02QIimmIlAiEx/m+eCDofjbsiXMlzBxInznO6HwGzu2cQ6F730Prrqqea9gjx5hFlCRw2Fm5wKLgEJgibvf3OJ5iz3/KWAPcIW7rzWzYuAZoBshf/6vu38rrcFLerz1FjzwQCj8nngi9AD26weXXBIKv2nTwjIPAF/8Yuuzg4pI5N778D2qtlSxfPNylm9ezqo3V7G/fj8AJ5acyIiSEby842XqvZ6uhV25ctyVzB03N+KoRVIrpUWgLrSkpUTDPI86Cj75SbjxRpgxA449NvFr58wJP+Ozg5aXhwIwvl+kI8ysEPg58EmgBlhlZg+4+4tNDpsOHB/bTgN+Efu5DzjL3XeZWRGw3Mz+5O7PprURkhovvxyKvmXL4NlnwxCFoUPh2mtD4Td5cuLFRmtr0xyoiLTG3dm0c9PBgm/55uVUv1sNQFFBEacOPJUvTfwSFeUVTC2fSkmPErZ9sI2htw6lvq6eQivUjJ2SF1JWBOpCS6BxmGe8ty/RMM8zzoDi4o6935w5Kvqk0yYCG939NQAzuxc4H2iam84Hfu3uDjxrZn3MbIC7bwN2xY4pim2evtAlqRoaYM0auP/+UPhtCDP+ccop8O1vh8Jv1Cgt6SCSweob6ln3zrpmRV98opfe3XoztWwqs8fMZmrZVCYMmkCPoh6HvMeAXgOYe/JcrdsneSWVPYG60MoDTdfti/fMXXQR/PWvoehrOczzu98Nhd+YMbquksgMAppOJ1RD+PKpvWMGAdtiX3CtAT4G/Nzdn0thrHIk2lq8fft2ePrpxoldtm4NvXuf+ARcfTWcf75uNJZIHekoqibPFwKrga3ufl7aAk+S1mbrHNRrEDXX1bDnwB5Wbl15sOCr2lLFB/vDf++lvUs5ffDpVJRXUFFewah+oygsSNB7n4DW7ZN8k8oiMC0XWmZ2FXAVQLn+x51WS5c2v0dv0yb4/Odh/nzYty8M8zz77PaHeYqkWaKvH1p+ydTqMe5eD5xsZn2A+81stLuvP+Qkyk3RaWvx9v79YefOcEPxueeG3r4ZM+Doo9MaokginRxFFfdlYAPQOy1BJ9ms4bO48x93HrxnD6BLQRdKepQwackk1mxbQ11DHYYxuv9oPjf2cweLvvKPHHmu1bp9km9SWQSm5ULL3e8A7gAYP368egtTzD3c17d+fbhNpuXSDfX1Ya6EZcsOb5inSBrVAGVNHpcCbx7uMe7+LzN7CjgXUG7KFN7On/qii0LhV1nZOLGLSObo1CgqMysFZgA3AdelOfZOa/AGLh11KYvXLm62v66hjpe2v8TEQRP56pSvUlFeweTSyfTt3jeiSEWyXyqLwLRcaEnqvP8+VFfDunWh6Itv773X9ut27w5fsItkqFXA8WY2BNgKXArMbnHMA8A1sQuw04CdsQusfsCBWF7qDlQCt6QxdolzD0M5q6ubby++2Pbr7rwzPfGJHJlOjaICfgLcALQ6NW0mjVLYtX8XK7eupGpLFStqVrBiywre3/t+s2MKrZBZw2fx24t+qzX6RJIolUWgLrSyxJ49YT6E9eubF3xbmwzJ790bRo8Os6OPHh22yy6DmppD308j3ySTuXudmV0DPEa45+aX7l5tZgtiz98OPEK432Yj4Z6b+FzhA4BfxYZsFQC/c/eH0t2GvOIeFmRPVOzt3Nl4XP/+YRKXK66An/40snBFOumIR1GZ2XnAO+6+xszOaO0EUY1ScHde/9froeDbsoKqmipeePsFGrwBgFH9RnHRiIuYUjaFYUcP4+z/OZt99fvoWtiV22bcpgJQJMlSVgTqQisaiSZqic+meeAAvPJKY5EXL/hefbVxBFW3bjByJJx1Vpi8JV7wlZYeOpHLzTdr3T7JTu7+CCH/NN13e5PfHfj3BK97ARiX8gBzTVsTtcSXV3CHd945tNirrg7DEuKOOSYkpTlzQtEX30pKGo9RESjZqzOjqC4GZpnZp4BioLeZ3ePun0thvK3aW7eXNW+uoWpLFVU1ofB7e/fbAPTs2pNJpZP45se/yeSyyZw26LRDhnbOGzdPs3WKpJB5e/dPZJHx48f76tWr2zymrSIpFdJ5vpYTtQAUFcH48bBrF7z0UigEIUyGd/zx4VqqabE3bFjiZbDaOqfW7ZNUMrM17j4+6jg6o93c1JEiKZnSfb62pgK++urGYm/Hjsb9ffs2L/LiW//+7U8tnO72SV5KRW4ysy7Ay8A0wiiqVcBsd69ucswM4BrCl+inAbe6+8QW73MG8JX2ZgftyHVTe7N1xtXU1oQevtjQzrXb1nKgIVx0fOzojzG5dDJTyqYwpWxKh2bt3PbBNiruquDv8/6uIlDkMHUkP6V0sfhMk2g2y6uuCr+nonA5nPMdOBCuWT74IBRs8d9bbm09t2FDmJil5fuuXBnu0Zsxo7HYGz48OZO2aN0+kSRoazbLTDmfe7jhd9eu5ls8MbW1ry1Ll4bi7tOfbl7sffSjR76OjAo9yVKdHEWVEolm6ywqKGJS6SQWPbuIFTWh8NtSG25TLO5SzISBE7hu8nVMKZvCpNJJ9D+q/2GfV7N1iqRWXvUEHndcKMRaKimBRYtCAdXQ0PGf7R1zxx2Jr6m6dQtFWNMCbt++jrWxsDB8md1069kz/Fy2LPFrzEJMItkoL3oC2yp27r03FGDxpBPfWu47nGP+8z9bP9/06YmLud272595My6eqOLJKb4IeyINDVo0VLJSXuQmQo/ckEVD2Fef+EKlrHcZU8qmHOzpO+mjJ9G1sGsqwhWRDlJPYAubNyfev317cnqzCguhoKDxZ8vlE+L27YMhQxIXcq1t8eeLi1u/XmqtyNVELSJZ7NJL03u+d98NCae0NPyMb/FE1Na++OOuXZsnqraKPBWAIhltQK8BnH/i+fyu+ncAGMaY/mP45unhfr7S3qURRygiRyKvisDy8sRF0sCB8OSTzQu49n623JfoOqa1omzw4NZ77Trjpps0UYtIzqmubkw6BQUh2TR9nGhfe8d0a2OWvVWr0tc2EckKPz77xyx7aRn76/dT3KWYxy57TPfpiWS5vCoCWyuSfvADOOGE9J0vVUVZvDdTE7WI5JCRI6OOoPN69Wp9ohYRyXiDeg9i/rj5mq1TJIcURB1AOs2ZE+7TGzw4fCk+eHB4nKoiKd3ni5/zjTfCbTZvvKECUCQrtFYMpapISvf5amvD/YQtN03gIpI1Fp6+kOP6HMfCTyyMOhQRSYK86gmE9M9mqdkzRaRd6S6GVHyJyGHSbJ0iuSWvegJFRERERETynYpAERERERGRPKIiUEREREREJI+oCBQREREREckjKgJFRERERETyiIpAERERERGRPKIiUEREREREJI+oCBQREREREckj5u5Rx5A0ZvYusCnqOFpRAmyPOogUUvuyWya3b7C794s6iM5QboqU2pfdMrl9yk2pl8mffzLkcvtyuW2Q+e1rNz/lVBGYycxstbuPjzqOVFH7sluut09al+ufvdqX3XK9fdK2XP/8c7l9udw2yI32aTioiIiIiIhIHlERKCIiIiIikkdUBKbPHVEHkGJqX3bL9fZJ63L9s1f7sluut0/aluuffy63L5fbBjnQPt0TKCIiIiIikkfUEygiIiIiIpJHVAQmkZmda2b/Z2YbzezrCZ6fY2YvxLYqMzspijiPVHvta3LcBDOrN7OL0xlfZ3WkfWZ2hpn908yqzezpdMfYGR349/kRM3vQzJ6PtW9uFHFKaig/HTxO+SkDKT/lL+Wmg8cpN2WgnM5N7q4tCRtQCLwKDAW6As8DI1scMwXoG/t9OvBc1HEns31Njvsr8AhwcdRxJ/nz6wO8CJTHHvePOu4kt+8bwC2x3/sB7wFdo45dW9o+f+WnDN2Un5SfcnVTbmp2nHJThm25npvUE5g8E4GN7v6au+8H7gXOb3qAu1e5+/uxh88CpWmOsTPabV/MtcDvgXfSGVwSdKR9s4E/uPtmAHfPpjZ2pH0O9DIzA3oSElldesOUFFF+CpSfMpPyU/5SbgqUmzJTTucmFYHJMwjY0uRxTWxfa+YDf0ppRMnVbvvMbBBwIXB7GuNKlo58ficAfc3sKTNbY2aXpy26zutI+34GjADeBNYBX3b3hvSEJymm/KT8lMmUn/KXcpNyUybL6dzUJeoAcogl2Jdw6lUzO5OQyCpSGlFydaR9PwG+5u714QuRrNKR9nUBTgWmAd2BFWb2rLu/nOrgkqAj7TsH+CdwFjAM+IuZ/c3da1Mcm6Se8pPyUyZTfspfyk3KTZksp3OTisDkqQHKmjwuJXwr0IyZjQWWANPdfUeaYkuGjrRvPHBvLImVAJ8yszp3X5aWCDunI+2rAba7+25gt5k9A5wEZEMi60j75gI3exjYvtHMXgdOBFamJ0RJIeUn5adMpvyUv5SblJsyWW7npqhvSsyVjVBQvwYMofHm0VEtjikHNgJToo43Fe1rcfzdZNfNzR35/EYAT8SO7QGsB0ZHHXsS2/cL4MbY78cCW4GSqGPXlrbPX/kpQzflJ+WnXN2Umw45Xrkpg7Zcz03qCUwSd68zs2uAxwizCf3S3avNbEHs+duB/wKOAW6LfeNT5+7jo4r5cHSwfVmrI+1z9w1m9ijwAtAALHH39dFF3XEd/Py+A9xtZusIQyC+5u7bIwtakkb5Sfkpkyk/5S/lJuWmTJbruclilauIiIiIiIjkAc0OKiIiIiIikkdUBIqIiIiIiOQRFYEiIiIiIiJ5REWgiIiIiIhIHlERKCIiIiIikkdUBEpSmVmpmf3RzF4xs1fNbJGZdY06LhHJb8pNIpKJlJskKioCJWksLODzB2CZux8PnAD0BG6KNDARyWvKTSKSiZSbJEoqAiWZzgL2uvtdAO5eD/wHMM/MjjKzH5rZOjN7wcyuBTCzCWZWZWbPm9lKM+tlZleY2c/ib2pmD5nZGbHfd5nZj8xsrZk9YWb9Yvv/zcxWxd7n92bWI7b/bjO7NXaO18zs4ibve0MsnufN7GYzG2Zma5s8f7yZrUn5X01EUk25SUQykXKTREZFoCTTKKDZf/zuXgtsBq4EhgDj3H0ssDQ23OE+4MvufhJQCXzYzjmOAta6+ynA08C3Yvv/4O4TYu+zAZjf5DUDgArgPOBmADObDlwAnBZ7zQ/c/VVgp5mdHHvdXODuw/kDiEhGUm4SkUyk3CSRUREoyWSAt7L/dOB2d68DcPf3gOHANndfFdtXG3++DQ2EBAhwDyFJAYw2s7+Z2TpgDiGxxi1z9wZ3fxE4NravErjL3fc0iQdgCTDXzAqBzwC/6UC7RSSzKTeJSCZSbpLIqAiUZKoGxjfdYWa9gTISJ7rWkl8dzf9tFrdxzvjr7waucfcxwH+3eM2+Fuds69y/B6YTvv1a4+472ji3iGQH5SYRyUTKTRIZFYGSTE8APczscoDYt0I/IiSaPwMLzKxL7LmjgZeAgWY2IbavV+z5N4CTzazAzMqAiU3OUQDEx6fPBpbHfu8FbDOzIsI3Wu35M2HMfXwM/NEA7r4XeAz4BXDX4f4BRCQjKTeJSCZSbpLIdIk6AMkd7u5mdiFwm5ktJCSeR4BvAPWEWa9eMLMDwGJ3/5mZfQb4qZl1J4xrrwT+DrwOrAPWA2ubnGY3MCp24/FOwtADgIXAc8Cm2Ot6tRPro7Ex7KvNbH+TOAGWAp8mJDwRyXLKTSKSiZSbJErmnqhnVyQzmdkud++Z4nN8BfiIuy9M5XlEJHcoN4lIJlJuktaoJ1CkCTO7HxhGmLZZRCQjKDeJSCZSbspe6gkUERERERHJI5oYRkREREREJI+oCBQREREREckjKgJFRERERETyiIpAERERERGRPKIiUEREREREJI+oCBQREREREckj/w+PJCvQ+QRjuwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_retrieve_occupancy\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `retrieve` performance by varying matching rates\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_retrieve_matching_rate\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"MatchingRate\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `query` (`retrieve` + `count`) performance by varying key muliplicities\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
  • Fixed occupancy: 0.8
    • \n", + "
  • UNIFORM distribution
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_query_uniform_multiplicity\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_single_perf(bm, tmpdf, \"Multiplicity\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `query` (`retrieve` + `count`) performance by varying occupancies\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_query_occupancy\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"Occupancy\", unique_labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `query` (`retrieve` + `count`) performance by varying matching rates\n", + "
    \n", + "
  • 100'000'000 key/value pairs inserted
    • \n", + "
  • 100'000'000 probing keys
    • \n", + "
  • Fixed matching rate: 0.5
    • \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for bm in unique_bms:\n", + " flag = \"staic_multimap_query_matching_rate\" == bm\n", + " \n", + " if flag:\n", + " tmpdf = perfdf[perfdf[\"Benchmark\"] == bm]\n", + " \n", + " unique_labels = tmpdf[\"Label\"].unique()\n", + " plot_dual_perf(bm, tmpdf, \"MatchingRate\", unique_labels)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/benchmarks/analysis/notebooks/Utils.py b/benchmarks/analysis/notebooks/Utils.py new file mode 100644 index 000000000..3e6a001e8 --- /dev/null +++ b/benchmarks/analysis/notebooks/Utils.py @@ -0,0 +1,99 @@ +# Import libraries +import pandas as pd +import matplotlib.pyplot as plt +import matplotlib + +# Global parameters +colors = ['b','r','g','m','y','c'] +styles = ['o','s','v','^','D',">"] + +def plot_single_perf(bm, df, xaxis, unique_labels): + fig = fig = plt.figure(1,figsize=(5, 5)) + fig.suptitle(bm) + + ax = fig.gca() + ax.set_xlabel(xaxis) + ax.set_ylabel('GPU Time (sec)') + + ax.set_xscale('log') + ax.set_xticks(list(df[xaxis])) + ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + + marker_handles = [] + + num_style = len(df["Distribution"].unique()) + + # Iterate over labels and label indices + for lindex, lbl in enumerate(unique_labels): + tmpdf = df.loc[df['Label'] == lbl] + + x = tmpdf[xaxis] + perf = tmpdf["GPU Time (sec)"] + + # Get style & type index + sid = lindex % num_style + tid = int(lindex / num_style) + + if not tid: + ax.plot(x, perf, color=colors[sid]) + ax.scatter(x, perf, color=colors[sid], marker=styles[sid]) + + # Add legend + marker_handles.append(ax.plot([], [], c=colors[sid], marker=styles[sid], \ + label=lbl)[0]) + else: + ax.plot(x, perf, color=colors[sid], linestyle="--") + ax.scatter(x, perf, color=colors[sid], marker=styles[sid], facecolors='none') + + # Add legend + marker_handles.append(ax.plot([], [], c=colors[sid], marker=styles[sid], \ + mfc='none', linestyle="--", label=lbl)[0]) + + leg = plt.legend(handles = marker_handles, loc="upper left", ncol=2, frameon=False) + plt.savefig(bm + '.eps') + +def plot_dual_perf(bm, df, xaxis, unique_labels): + fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5)) + fig.suptitle(bm) + + marker_handles = [] + + lax = [ax1, ax2, ax3] + + for item in lax: + item.set_xlabel(xaxis) + item.set_ylabel("GPU Time (sec)") + + num_style = len(df["Distribution"].unique()) + + # Iterate over labels and label indices + for lindex, lbl in enumerate(unique_labels): + tmpdf = df.loc[df['Label'] == lbl] + + x = tmpdf[xaxis] + perf = tmpdf["GPU Time (sec)"] + + # Get style & type index + sid = lindex % num_style + tid = int(lindex / num_style) + + # INT32 + if not tid: + lax[sid].plot(x, perf, color=colors[sid]) + lax[sid].scatter(x, perf, color=colors[sid], marker=styles[sid]) + + # Add legend + marker_handles.append(lax[sid].plot([], [], c=colors[sid], marker=styles[sid], \ + label=lbl)[0]) + # INT64 + else: + + lax[sid].plot(x, perf, color=colors[sid], linestyle="--") + lax[sid].scatter(x, perf, color=colors[sid], marker=styles[sid], facecolors='none') + + # Add legend + marker_handles.append(lax[sid].plot([], [], c=colors[sid], marker=styles[sid], \ + mfc='none', linestyle="--", label=lbl)[0]) + + leg = plt.legend(handles = marker_handles, loc="upper left", ncol=2, frameon=False) + plt.savefig(bm + '.eps') \ No newline at end of file diff --git a/benchmarks/hash_table/static_multimap/retrieve_bench.cu b/benchmarks/hash_table/static_multimap/retrieve_bench.cu new file mode 100644 index 000000000..5856ed7c5 --- /dev/null +++ b/benchmarks/hash_table/static_multimap/retrieve_bench.cu @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include + +#include "cuco/static_multimap.cuh" + +/** + * @brief Generates input keys by a given number of repetitions per key. + * + */ +template +static void generate_multikeys(OutputIt output_begin, + OutputIt output_end, + size_t const multiplicity) +{ + auto num_keys = std::distance(output_begin, output_end); + + for (auto i = 0; i < num_keys; ++i) { + output_begin[i] = (i % (num_keys / multiplicity)) + 1; + } +} + +/** + * @brief A benchmark evaluating multi-value retrieval performance by varing number of repetitions + * per key: + * - 100'000'000 keys are inserted + * - Map occupancy is fixed at 0.4 + * - Number of repetitions per key: 1, ... , 128, 256 + * + */ +template +std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_retrieve( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + std::size_t const num_keys = state.get_int64("NumInputs"); + auto const occupancy = state.get_float64("Occupancy"); + std::size_t const size = num_keys / occupancy; + std::size_t const multiplicity = state.get_int64("Multiplicity"); + + state.add_element_count(num_keys, "NumKeys"); + state.add_global_memory_writes(num_keys * 2); + + std::vector h_keys(num_keys); + std::vector> h_pairs(num_keys); + + generate_multikeys(h_keys.begin(), h_keys.end(), multiplicity); + for (auto i = 0; i < num_keys; ++i) { + Key key = h_keys[i]; + Value val = h_keys[i]; + h_pairs[i].first = key; + h_pairs[i].second = val; + } + + thrust::device_vector d_keys(h_keys); + thrust::device_vector> d_pairs(h_pairs); + + cuco::static_multimap, + cuco::double_hashing, + cuco::detail::MurmurHash3_32>> + map{size, -1, -1}; + map.insert(d_pairs.begin(), d_pairs.end()); + + auto const output_size = map.count_outer(d_keys.begin(), d_keys.end()); + thrust::device_vector> d_results(output_size); + + state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { + map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream()); + }); +} + +template +std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_retrieve( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + state.skip("Key should be the same type as Value."); +} + +using key_type = nvbench::type_list; +using value_type = nvbench::type_list; +using cg_size = nvbench::enum_type_list<1, 2, 4, 8, 16, 32>; +using buffer_size = nvbench::enum_type_list<1, 2, 4, 8, 16>; + +NVBENCH_BENCH_TYPES(nvbench_retrieve, + NVBENCH_TYPE_AXES(key_type, value_type, cg_size, nvbench::enum_type_list<2>)) + .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.4}) + .add_int64_power_of_two_axis("Multiplicity", nvbench::range(0, 8, 1)); + +NVBENCH_BENCH_TYPES( + nvbench_retrieve, + NVBENCH_TYPE_AXES(key_type, value_type, nvbench::enum_type_list<8>, buffer_size)) + .set_type_axes_names({"Key", "Value", "CGSize", "BufferSize"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.4}) + .add_int64_power_of_two_axis("Multiplicity", nvbench::range(0, 8, 1)); diff --git a/benchmarks/hash_table/static_multimap/static_multimap_bench.cu b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu new file mode 100644 index 000000000..4ba873147 --- /dev/null +++ b/benchmarks/hash_table/static_multimap/static_multimap_bench.cu @@ -0,0 +1,450 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include +#include + +#include +#include + +namespace { +// Custom pair equal +template +struct pair_equal { + __device__ bool operator()(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) const + { + return lhs.first == rhs.first; + } +}; +} // anonymous namespace + +/** + * @brief A benchmark evaluating multi-value `insert` performance: + * - Total number of insertions: 100'000'000 + * - CG size: 8 + */ +template +std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_insert( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + auto const num_keys = state.get_int64("NumInputs"); + auto const occupancy = state.get_float64("Occupancy"); + + std::size_t const size = num_keys / occupancy; + + std::vector h_keys(num_keys); + std::vector> h_pairs(num_keys); + + generate_keys(h_keys.begin(), h_keys.end()); + + for (auto i = 0; i < num_keys; ++i) { + Key key = h_keys[i]; + Value val = h_keys[i]; + h_pairs[i].first = key; + h_pairs[i].second = val; + } + + thrust::device_vector> d_pairs(h_pairs); + + state.add_element_count(num_keys, "NumKeys"); + + state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer, + [&](nvbench::launch& launch, auto& timer) { + cuco::static_multimap map{size, -1, -1}; + + // Use timers to explicitly mark the target region + timer.start(); + map.insert(d_pairs.begin(), d_pairs.end(), launch.get_stream()); + timer.stop(); + }); +} + +template +std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_insert( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + state.skip("Key should be the same type as Value."); +} + +/** + * @brief A benchmark evaluating multi-value `count` performance: + * - Total number of insertions: 100'000'000 + * - CG size: 8 + */ +template +std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_count( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + auto const num_keys = state.get_int64("NumInputs"); + auto const occupancy = state.get_float64("Occupancy"); + auto const matching_rate = state.get_float64("MatchingRate"); + + std::size_t const size = num_keys / occupancy; + + std::vector h_keys(num_keys); + std::vector> h_pairs(num_keys); + + generate_keys(h_keys.begin(), h_keys.end()); + + for (auto i = 0; i < num_keys; ++i) { + Key key = h_keys[i]; + Value val = h_keys[i]; + h_pairs[i].first = key; + h_pairs[i].second = val; + } + + generate_probe_keys(matching_rate, h_keys.begin(), h_keys.end()); + + thrust::device_vector d_keys(h_keys); + thrust::device_vector> d_pairs(h_pairs); + + state.add_element_count(num_keys, "NumKeys"); + + cuco::static_multimap map{size, -1, -1}; + map.insert(d_pairs.begin(), d_pairs.end()); + + state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { + auto count = map.count(d_keys.begin(), d_keys.end(), launch.get_stream()); + }); +} + +template +std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_count( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + state.skip("Key should be the same type as Value."); +} + +/** + * @brief A benchmark evaluating multi-value `retrieve` performance: + * - Total number of insertions: 100'000'000 + * - CG size: 8 + */ +template +std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_retrieve( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + auto const num_keys = state.get_int64("NumInputs"); + auto const occupancy = state.get_float64("Occupancy"); + auto const matching_rate = state.get_float64("MatchingRate"); + + std::size_t const size = num_keys / occupancy; + + std::vector h_keys(num_keys); + std::vector> h_pairs(num_keys); + + generate_keys(h_keys.begin(), h_keys.end()); + + for (auto i = 0; i < num_keys; ++i) { + Key key = h_keys[i]; + Value val = h_keys[i]; + h_pairs[i].first = key; + h_pairs[i].second = val; + } + + generate_probe_keys(matching_rate, h_keys.begin(), h_keys.end()); + + thrust::device_vector d_keys(h_keys); + thrust::device_vector> d_pairs(h_pairs); + + state.add_element_count(num_keys, "NumKeys"); + + cuco::static_multimap map{size, -1, -1}; + map.insert(d_pairs.begin(), d_pairs.end()); + + auto const output_size = map.count_outer(d_keys.begin(), d_keys.end()); + thrust::device_vector> d_results(output_size); + + state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { + map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream()); + }); +} + +template +std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_retrieve( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + state.skip("Key should be the same type as Value."); +} + +/** + * @brief A benchmark evaluating multi-value query (`count` + `retrieve`) performance: + * - Total number of insertions: 100'000'000 + * - CG size: 8 + */ +template +std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_query( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + auto const num_keys = state.get_int64("NumInputs"); + auto const occupancy = state.get_float64("Occupancy"); + auto const matching_rate = state.get_float64("MatchingRate"); + + std::size_t const size = num_keys / occupancy; + + std::vector h_keys(num_keys); + std::vector> h_pairs(num_keys); + + generate_keys(h_keys.begin(), h_keys.end()); + + for (auto i = 0; i < num_keys; ++i) { + Key key = h_keys[i]; + Value val = h_keys[i]; + h_pairs[i].first = key; + h_pairs[i].second = val; + } + + generate_probe_keys(matching_rate, h_keys.begin(), h_keys.end()); + + thrust::device_vector d_keys(h_keys); + thrust::device_vector> d_pairs(h_pairs); + + state.add_element_count(num_keys, "NumKeys"); + + cuco::static_multimap map{size, -1, -1}; + map.insert(d_pairs.begin(), d_pairs.end()); + + auto const output_size = map.count_outer(d_keys.begin(), d_keys.end()); + thrust::device_vector> d_results(output_size); + + state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { + auto count = map.count_outer(d_keys.begin(), d_keys.end(), launch.get_stream()); + map.retrieve_outer(d_keys.begin(), d_keys.end(), d_results.data().get(), launch.get_stream()); + }); +} + +template +std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_query( + nvbench::state& state, + nvbench::type_list, nvbench::enum_type>) +{ + state.skip("Key should be the same type as Value."); +} + +/** + * @brief A benchmark evaluating `pair_retrieve` performance: + * - CG size: 8 + */ +template +std::enable_if_t<(sizeof(Key) == sizeof(Value)), void> nvbench_static_multimap_pair_retrieve( + nvbench::state& state, nvbench::type_list>) +{ + auto constexpr matching_rate = 0.5; + auto constexpr occupancy = 0.5; + auto constexpr dist = dist_type::UNIFORM; + + auto const num_input = state.get_int64("NumInputs"); + + std::size_t const size = num_input / occupancy; + + std::vector h_keys(num_input); + std::vector> h_pairs(num_input); + + generate_keys(h_keys.begin(), h_keys.end()); + + for (auto i = 0; i < num_input; ++i) { + Key key = h_keys[i]; + Value val = h_keys[i]; + h_pairs[i].first = key; + h_pairs[i].second = val; + } + + thrust::device_vector> d_pairs(h_pairs); + auto const pair_begin = d_pairs.begin(); + + cuco::static_multimap map{size, -1, -1}; + map.insert(pair_begin, pair_begin + num_input); + + generate_probe_keys(matching_rate, h_keys.begin(), h_keys.end()); + thrust::device_vector d_keys(h_keys); + + thrust::transform( + thrust::device, d_keys.begin(), d_keys.begin() + num_input, pair_begin, [] __device__(Key i) { + return cuco::pair_type{i, i}; + }); + + state.add_element_count(num_input, "NumInputs"); + + auto const output_size = + map.pair_count(pair_begin, pair_begin + num_input, pair_equal{}); + thrust::device_vector> d_results(output_size); + + auto out1_begin = thrust::make_zip_iterator( + thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator())); + auto out2_begin = thrust::make_zip_iterator( + thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator())); + + state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { + auto [out1_end, out2_end] = map.pair_retrieve( + pair_begin, pair_begin + num_input, out1_begin, out2_begin, pair_equal{}); + }); +} + +template +std::enable_if_t<(sizeof(Key) != sizeof(Value)), void> nvbench_static_multimap_pair_retrieve( + nvbench::state& state, nvbench::type_list>) +{ + state.skip("Key should be the same type as Value."); +} + +using key_type = nvbench::type_list; +using value_type = nvbench::type_list; +using d_type = + nvbench::enum_type_list; + +using multiplicity = nvbench::enum_type_list<1, 2, 4, 8, 16, 32, 64, 128, 256>; + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, + NVBENCH_TYPE_AXES(key_type, + value_type, + nvbench::enum_type_list, + multiplicity)) + .set_name("staic_multimap_insert_uniform_multiplicity") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_insert, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_insert_occupancy") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1)); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, + NVBENCH_TYPE_AXES(key_type, + value_type, + nvbench::enum_type_list, + multiplicity)) + .set_name("staic_multimap_count_uniform_multiplicity") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}) + .add_float64_axis("MatchingRate", {0.5}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_count_occupancy") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1)) + .add_float64_axis("MatchingRate", {0.5}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_count, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_count_matching_rate") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}) + .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, + NVBENCH_TYPE_AXES(key_type, + value_type, + nvbench::enum_type_list, + multiplicity)) + .set_name("staic_multimap_retrieve_uniform_multiplicity") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}) + .add_float64_axis("MatchingRate", {0.5}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_retrieve_occupancy") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1)) + .add_float64_axis("MatchingRate", {0.5}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_retrieve, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_retrieve_matching_rate") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}) + .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_query, + NVBENCH_TYPE_AXES(key_type, + value_type, + nvbench::enum_type_list, + multiplicity)) + .set_name("staic_multimap_query_uniform_multiplicity") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}) + .add_float64_axis("MatchingRate", {0.5}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_query, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_query_occupancy") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", nvbench::range(0.1, 0.9, 0.1)) + .add_float64_axis("MatchingRate", {0.5}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_query, + NVBENCH_TYPE_AXES(key_type, value_type, d_type, nvbench::enum_type_list<8>)) + .set_name("staic_multimap_query_matching_rate") + .set_type_axes_names({"Key", "Value", "Distribution", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", {100'000'000}) // Total number of key/value pairs: 100'000'000 + .add_float64_axis("Occupancy", {0.8}) + .add_float64_axis("MatchingRate", {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}); + +NVBENCH_BENCH_TYPES(nvbench_static_multimap_pair_retrieve, + NVBENCH_TYPE_AXES(key_type, value_type, multiplicity)) + .set_name("staic_multimap_pair_retrieve_uniform_multiplicity") + .set_type_axes_names({"Key", "Value", "Multiplicity"}) + .set_timeout(100) // Custom timeout: 100 s. Default is 15 s. + .set_max_noise(3) // Custom noise: 3%. By default: 0.5%. + .add_int64_axis("NumInputs", + {1'000, + 100'000, + 1'000'000, + 10'000'000, + 100'000'000}); // Total number of key/value pairs: 100'000'000 diff --git a/benchmarks/key_generator.hpp b/benchmarks/key_generator.hpp new file mode 100644 index 000000000..09edb2654 --- /dev/null +++ b/benchmarks/key_generator.hpp @@ -0,0 +1,112 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +enum class dist_type { GAUSSIAN, GEOMETRIC, UNIFORM }; + +NVBENCH_DECLARE_ENUM_TYPE_STRINGS( + // Enum type: + dist_type, + // Callable to generate input strings: + // Short identifier used for tables, command-line args, etc. + // Used when context is available to figure out the enum type. + [](dist_type d) { + switch (d) { + case dist_type::GAUSSIAN: return "GAUSSIAN"; + case dist_type::GEOMETRIC: return "GEOMETRIC"; + case dist_type::UNIFORM: return "UNIFORM"; + default: return "ERROR"; + } + }, + // Callable to generate descriptions: + // If non-empty, these are used in `--list` to describe values. + // Used when context may not be available to figure out the type from the + // input string. + // Just use `[](auto) { return std::string{}; }` if you don't want these. + [](auto) { return std::string{}; }) + +template +static void generate_keys(OutputIt output_begin, OutputIt output_end) +{ + auto const num_keys = std::distance(output_begin, output_end); + + std::random_device rd; + std::mt19937 gen{rd()}; + + switch (Dist) { + case dist_type::GAUSSIAN: { + auto const mean = static_cast(num_keys / 2); + auto const dev = static_cast(num_keys / 5); + + std::normal_distribution<> distribution{mean, dev}; + + for (auto i = 0; i < num_keys; ++i) { + auto k = distribution(gen); + while (k >= num_keys) { + k = distribution(gen); + } + output_begin[i] = k; + } + break; + } + case dist_type::GEOMETRIC: { + auto const max = std::numeric_limits::max(); + auto const coeff = static_cast(num_keys) / static_cast(max); + // Random sampling in range [0, INT32_MAX] + std::geometric_distribution distribution{1e-9}; + + for (auto i = 0; i < num_keys; ++i) { + output_begin[i] = distribution(gen) * coeff; + } + break; + } + case dist_type::UNIFORM: { + std::uniform_int_distribution distribution{1, static_cast(num_keys / Multiplicity)}; + + for (auto i = 0; i < num_keys; ++i) { + output_begin[i] = distribution(gen); + } + break; + } + } // switch +} + +template +static void generate_probe_keys(double const matching_rate, + OutputIt output_begin, + OutputIt output_end) +{ + auto const num_keys = std::distance(output_begin, output_end); + auto const max = std::numeric_limits::max(); + + std::random_device rd; + std::mt19937 gen{rd()}; + + std::uniform_real_distribution rate_dist(0.0, 1.0); + std::uniform_int_distribution non_match_dist{static_cast(num_keys), max}; + + for (auto i = 0; i < num_keys; ++i) { + auto const tmp_rate = rate_dist(gen); + + if (tmp_rate > matching_rate) { output_begin[i] = non_match_dist(gen); } + } + + std::random_shuffle(output_begin, output_end); +} diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index f8598900b..aec370508 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -18,3 +18,4 @@ endfunction(ConfigureExample) ################################################################################################### ConfigureExample(STATIC_MAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_map/static_map_example.cu") +ConfigureExample(STATIC_MULTIMAP_EXAMPLE "${CMAKE_CURRENT_SOURCE_DIR}/static_multimap/static_multimap_example.cu") diff --git a/examples/static_multimap/static_multimap_example.cu b/examples/static_multimap/static_multimap_example.cu new file mode 100644 index 000000000..cda8f2073 --- /dev/null +++ b/examples/static_multimap/static_multimap_example.cu @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include +#include + +#include + +int main(void) +{ + using key_type = int; + using value_type = int; + + int empty_key_sentinel = -1; + int empty_value_sentinel = -1; + + constexpr std::size_t N = 50'000; + + // Constructs a multimap with 100,000 slots using -1 and -1 as the empty key/value + // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys, + // for an load factor of 50%. + cuco::static_multimap map{N * 2, empty_key_sentinel, empty_value_sentinel}; + + thrust::device_vector> pairs(N); + + // Create a sequence of pairs. Eeach key has two matches. + // E.g., {{0,0}, {1,1}, ... {0,25'000}, {1, 25'001}, ...} + thrust::transform(thrust::make_counting_iterator(0), + thrust::make_counting_iterator(pairs.size()), + pairs.begin(), + [] __device__(auto i) { return thrust::make_pair(i % (N / 2), i); }); + + // Inserts all pairs into the map + map.insert(pairs.begin(), pairs.end()); + + // Sequence of probe keys {0, 1, 2, ... 49'999} + thrust::device_vector keys_to_find(N); + thrust::sequence(keys_to_find.begin(), keys_to_find.end(), 0); + + // Counts the occurrences of keys in [0, 50'000) contained in the multimap. + // The `_outer` suffix indicates that the occurrence of a non-match is 1. + auto const output_size = map.count_outer(keys_to_find.begin(), keys_to_find.end()); + + thrust::device_vector> d_results(output_size); + + // Finds all keys {0, 1, 2, ...} and stores associated key/value pairs into `d_results` + // If a key `keys_to_find[i]` doesn't exist, `d_results[i].second == empty_value_sentinel` + auto output_end = + map.retrieve_outer(keys_to_find.begin(), keys_to_find.end(), d_results.data().get()); + auto retrieve_size = output_end - d_results.data().get(); + + // The total number of outer matches should be `N + N / 2` + assert(not(output_size == retrieve_size == N + N / 2)); + + return 0; +} diff --git a/include/cuco/allocator.hpp b/include/cuco/allocator.hpp index 21295c905..634e90423 100644 --- a/include/cuco/allocator.hpp +++ b/include/cuco/allocator.hpp @@ -29,16 +29,20 @@ class cuda_allocator { template cuda_allocator(cuda_allocator const&) noexcept - { } + { + } - value_type* allocate(std::size_t n) + value_type* allocate(std::size_t n, cudaStream_t stream = 0) { value_type* p; CUCO_CUDA_TRY(cudaMalloc(&p, sizeof(value_type) * n)); return p; } - void deallocate(value_type* p, std::size_t) { CUCO_CUDA_TRY(cudaFree(p)); } + void deallocate(value_type* p, std::size_t, cudaStream_t stream = 0) + { + CUCO_CUDA_TRY(cudaFree(p)); + } }; template diff --git a/include/cuco/detail/bitwise_compare.cuh b/include/cuco/detail/bitwise_compare.cuh index d554ddba3..03efb5fc4 100644 --- a/include/cuco/detail/bitwise_compare.cuh +++ b/include/cuco/detail/bitwise_compare.cuh @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. + * Copyright (c) 2020-2021, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,6 +14,8 @@ * limitations under the License. */ +#pragma once + #include #include diff --git a/include/cuco/detail/hash_functions.cuh b/include/cuco/detail/hash_functions.cuh index aec550aba..d5dcd0f64 100644 --- a/include/cuco/detail/hash_functions.cuh +++ b/include/cuco/detail/hash_functions.cuh @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017, NVIDIA CORPORATION. + * Copyright (c) 2017-2021, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -36,6 +36,8 @@ struct MurmurHash3_32 { __host__ __device__ constexpr MurmurHash3_32() : m_seed(0) {} + __host__ __device__ constexpr MurmurHash3_32(uint32_t seed) : m_seed(seed) {} + constexpr result_type __host__ __device__ operator()(Key const& key) const noexcept { constexpr int len = sizeof(argument_type); diff --git a/include/cuco/detail/pair.cuh b/include/cuco/detail/pair.cuh index 40d4aa8a4..0d8a85e3e 100644 --- a/include/cuco/detail/pair.cuh +++ b/include/cuco/detail/pair.cuh @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020, NVIDIA CORPORATION. + * Copyright (c) 2020-2021, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -14,6 +14,8 @@ * limitations under the License. */ +#pragma once + #include #include #include diff --git a/include/cuco/detail/prime.hpp b/include/cuco/detail/prime.hpp new file mode 100644 index 000000000..2dceba653 --- /dev/null +++ b/include/cuco/detail/prime.hpp @@ -0,0 +1,20194 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +namespace cuco { +namespace detail { + +constexpr std::array primes = { + 2, 3, 5, 7, 13, 19, 29, + 37, 43, 53, 59, 67, 73, 79, + 89, 97, 103, 109, 127, 137, 149, + 157, 163, 173, 179, 191, 197, 211, + 223, 229, 239, 251, 257, 263, 269, + 277, 283, 293, 307, 313, 331, 337, + 347, 353, 359, 367, 373, 379, 389, + 397, 409, 419, 431, 439, 449, 457, + 463, 479, 487, 499, 509, 521, 541, + 547, 557, 563, 569, 577, 587, 593, + 599, 607, 613, 619, 631, 641, 647, + 653, 659, 673, 683, 691, 701, 709, + 719, 727, 733, 739, 751, 757, 769, + 787, 797, 809, 821, 827, 839, 853, + 859, 877, 883, 907, 919, 929, 937, + 947, 953, 967, 977, 983, 991, 997, + 1009, 1019, 1031, 1039, 1049, 1061, 1069, + 1087, 1093, 1103, 1109, 1117, 1123, 1129, + 1151, 1163, 1171, 1181, 1187, 1193, 1201, + 1213, 1223, 1229, 1237, 1249, 1259, 1277, + 1283, 1289, 1297, 1303, 1319, 1327, 1361, + 1367, 1373, 1381, 1399, 1409, 1423, 1429, + 1439, 1447, 1453, 1459, 1471, 1481, 1487, + 1493, 1499, 1511, 1523, 1531, 1543, 1549, + 1559, 1567, 1579, 1597, 1607, 1613, 1619, + 1627, 1637, 1657, 1663, 1669, 1693, 1699, + 1709, 1721, 1733, 1741, 1747, 1753, 1759, + 1777, 1783, 1789, 1801, 1811, 1823, 1831, + 1847, 1861, 1867, 1873, 1879, 1889, 1901, + 1907, 1913, 1931, 1949, 1973, 1979, 1987, + 1993, 1999, 2011, 2017, 2027, 2039, 2053, + 2063, 2069, 2081, 2087, 2099, 2111, 2129, + 2137, 2143, 2153, 2161, 2179, 2203, 2213, + 2221, 2237, 2243, 2251, 2267, 2273, 2281, + 2287, 2293, 2309, 2333, 2339, 2347, 2357, + 2371, 2377, 2383, 2389, 2399, 2411, 2417, + 2423, 2437, 2447, 2459, 2467, 2473, 2503, + 2521, 2531, 2539, 2549, 2557, 2579, 2591, + 2609, 2617, 2633, 2647, 2657, 2663, 2671, + 2677, 2683, 2689, 2699, 2707, 2713, 2719, + 2729, 2741, 2749, 2767, 2777, 2789, 2797, + 2803, 2819, 2833, 2843, 2851, 2857, 2879, + 2887, 2897, 2903, 2909, 2917, 2927, 2939, + 2953, 2963, 2969, 2999, 3011, 3019, 3037, + 3049, 3061, 3067, 3079, 3089, 3109, 3119, + 3137, 3163, 3169, 3181, 3187, 3203, 3209, + 3217, 3229, 3251, 3257, 3271, 3299, 3307, + 3313, 3319, 3329, 3343, 3359, 3371, 3389, + 3407, 3413, 3433, 3449, 3457, 3463, 3469, + 3491, 3499, 3511, 3517, 3527, 3533, 3539, + 3547, 3557, 3571, 3581, 3593, 3607, 3613, + 3623, 3631, 3637, 3643, 3659, 3671, 3677, + 3691, 3697, 3709, 3719, 3727, 3733, 3739, + 3761, 3767, 3779, 3793, 3803, 3821, 3833, + 3847, 3853, 3863, 3877, 3889, 3907, 3917, + 3923, 3929, 3943, 3967, 3989, 4001, 4007, + 4013, 4019, 4027, 4049, 4057, 4073, 4079, + 4091, 4099, 4111, 4127, 4133, 4139, 4153, + 4159, 4177, 4201, 4211, 4217, 4229, 4241, + 4253, 4259, 4271, 4283, 4289, 4297, 4327, + 4337, 4349, 4357, 4363, 4373, 4391, 4397, + 4409, 4421, 4441, 4447, 4457, 4463, 4481, + 4493, 4507, 4513, 4519, 4547, 4561, 4567, + 4583, 4591, 4597, 4603, 4621, 4637, 4643, + 4649, 4657, 4663, 4673, 4679, 4691, 4703, + 4721, 4729, 4751, 4759, 4783, 4789, 4799, + 4813, 4831, 4861, 4871, 4877, 4889, 4903, + 4909, 4919, 4931, 4937, 4943, 4951, 4957, + 4967, 4973, 4987, 4993, 4999, 5009, 5021, + 5039, 5051, 5059, 5077, 5087, 5099, 5107, + 5113, 5119, 5147, 5153, 5167, 5179, 5189, + 5197, 5209, 5227, 5233, 5261, 5273, 5279, + 5297, 5303, 5309, 5323, 5333, 5347, 5381, + 5387, 5393, 5399, 5407, 5413, 5419, 5431, + 5437, 5443, 5449, 5471, 5477, 5483, 5501, + 5507, 5519, 5527, 5557, 5563, 5569, 5581, + 5591, 5623, 5639, 5647, 5653, 5659, 5669, + 5683, 5689, 5701, 5711, 5717, 5737, 5743, + 5749, 5779, 5791, 5801, 5807, 5813, 5821, + 5827, 5839, 5849, 5857, 5867, 5879, 5897, + 5903, 5923, 5939, 5953, 5981, 5987, 6007, + 6029, 6037, 6043, 6053, 6067, 6073, 6079, + 6089, 6101, 6113, 6121, 6131, 6143, 6151, + 6163, 6173, 6197, 6203, 6211, 6217, 6229, + 6247, 6257, 6263, 6269, 6277, 6287, 6299, + 6311, 6317, 6323, 6329, 6337, 6343, 6353, + 6359, 6367, 6373, 6379, 6389, 6397, 6421, + 6427, 6449, 6469, 6481, 6491, 6521, 6529, + 6547, 6553, 6563, 6569, 6577, 6599, 6607, + 6619, 6637, 6653, 6659, 6673, 6679, 6689, + 6701, 6709, 6719, 6733, 6761, 6779, 6791, + 6803, 6823, 6829, 6841, 6857, 6863, 6869, + 6883, 6899, 6907, 6917, 6947, 6959, 6967, + 6977, 6983, 6991, 6997, 7013, 7019, 7027, + 7039, 7057, 7069, 7079, 7103, 7109, 7121, + 7127, 7151, 7159, 7177, 7187, 7193, 7207, + 7213, 7219, 7229, 7237, 7243, 7253, 7283, + 7297, 7307, 7321, 7331, 7349, 7369, 7393, + 7411, 7417, 7433, 7451, 7457, 7477, 7487, + 7499, 7507, 7517, 7523, 7529, 7537, 7547, + 7559, 7573, 7583, 7589, 7603, 7621, 7639, + 7649, 7669, 7681, 7687, 7699, 7717, 7723, + 7741, 7753, 7759, 7789, 7817, 7823, 7829, + 7841, 7853, 7867, 7873, 7879, 7901, 7907, + 7919, 7927, 7933, 7949, 7963, 7993, 8009, + 8017, 8039, 8053, 8059, 8069, 8081, 8087, + 8093, 8101, 8111, 8117, 8123, 8147, 8161, + 8167, 8179, 8191, 8209, 8219, 8231, 8237, + 8243, 8263, 8269, 8287, 8293, 8311, 8317, + 8329, 8353, 8363, 8369, 8377, 8387, 8419, + 8429, 8443, 8461, 8467, 8501, 8513, 8521, + 8527, 8537, 8543, 8563, 8573, 8581, 8597, + 8609, 8623, 8629, 8641, 8647, 8663, 8669, + 8677, 8689, 8699, 8707, 8713, 8719, 8731, + 8737, 8747, 8753, 8761, 8779, 8803, 8819, + 8831, 8837, 8849, 8861, 8867, 8887, 8893, + 8923, 8929, 8941, 8951, 8963, 8969, 8999, + 9007, 9013, 9029, 9041, 9049, 9059, 9067, + 9091, 9103, 9109, 9127, 9133, 9151, 9157, + 9173, 9181, 9187, 9199, 9209, 9221, 9227, + 9239, 9257, 9277, 9283, 9293, 9311, 9319, + 9337, 9343, 9349, 9371, 9377, 9391, 9397, + 9403, 9413, 9419, 9431, 9437, 9461, 9467, + 9473, 9479, 9491, 9497, 9511, 9521, 9533, + 9539, 9547, 9587, 9601, 9613, 9619, 9629, + 9643, 9649, 9661, 9677, 9689, 9697, 9719, + 9733, 9739, 9749, 9767, 9781, 9787, 9803, + 9811, 9817, 9829, 9839, 9851, 9857, 9871, + 9883, 9901, 9907, 9923, 9929, 9941, 9949, + 9967, 9973, 10007, 10037, 10061, 10067, 10079, + 10091, 10099, 10111, 10133, 10139, 10151, 10159, + 10169, 10177, 10193, 10211, 10223, 10243, 10253, + 10259, 10267, 10273, 10289, 10301, 10313, 10321, + 10331, 10337, 10343, 10357, 10369, 10391, 10399, + 10427, 10433, 10453, 10459, 10477, 10487, 10499, + 10513, 10529, 10559, 10567, 10589, 10597, 10607, + 10613, 10627, 10639, 10651, 10657, 10663, 10687, + 10709, 10723, 10729, 10739, 10753, 10771, 10781, + 10789, 10799, 10831, 10837, 10847, 10853, 10859, + 10867, 10883, 10889, 10903, 10909, 10937, 10949, + 10957, 10973, 10979, 10987, 10993, 11003, 11027, + 11047, 11057, 11069, 11083, 11093, 11113, 11119, + 11131, 11149, 11159, 11171, 11177, 11197, 11213, + 11239, 11251, 11257, 11273, 11279, 11287, 11299, + 11311, 11317, 11329, 11351, 11369, 11383, 11393, + 11399, 11411, 11423, 11437, 11443, 11467, 11483, + 11489, 11497, 11503, 11519, 11527, 11549, 11579, + 11587, 11593, 11617, 11633, 11657, 11677, 11689, + 11699, 11717, 11731, 11743, 11777, 11783, 11789, + 11801, 11807, 11813, 11821, 11827, 11833, 11839, + 11863, 11887, 11897, 11903, 11909, 11923, 11933, + 11939, 11953, 11959, 11969, 11981, 11987, 12007, + 12037, 12043, 12049, 12071, 12097, 12107, 12113, + 12119, 12143, 12149, 12157, 12163, 12197, 12203, + 12211, 12227, 12239, 12251, 12263, 12269, 12277, + 12289, 12301, 12323, 12329, 12343, 12373, 12379, + 12391, 12401, 12409, 12421, 12433, 12451, 12457, + 12473, 12479, 12487, 12497, 12503, 12511, 12517, + 12527, 12539, 12547, 12553, 12569, 12577, 12583, + 12589, 12601, 12611, 12619, 12637, 12647, 12653, + 12659, 12671, 12689, 12697, 12703, 12713, 12721, + 12739, 12757, 12763, 12781, 12791, 12799, 12809, + 12821, 12829, 12841, 12853, 12889, 12899, 12907, + 12917, 12923, 12941, 12953, 12959, 12967, 12973, + 12979, 13001, 13007, 13033, 13043, 13049, 13063, + 13093, 13099, 13109, 13121, 13127, 13147, 13159, + 13171, 13177, 13183, 13217, 13229, 13241, 13249, + 13259, 13267, 13291, 13297, 13309, 13327, 13337, + 13367, 13381, 13397, 13411, 13417, 13441, 13451, + 13457, 13463, 13469, 13477, 13487, 13499, 13513, + 13523, 13537, 13553, 13567, 13577, 13591, 13597, + 13613, 13619, 13627, 13633, 13649, 13669, 13679, + 13687, 13693, 13709, 13721, 13729, 13751, 13757, + 13763, 13781, 13789, 13799, 13807, 13829, 13841, + 13859, 13873, 13879, 13901, 13907, 13913, 13921, + 13931, 13963, 13997, 14009, 14029, 14051, 14057, + 14071, 14081, 14087, 14107, 14143, 14149, 14159, + 14173, 14197, 14207, 14221, 14243, 14249, 14281, + 14293, 14303, 14321, 14327, 14341, 14347, 14369, + 14387, 14401, 14407, 14419, 14431, 14437, 14447, + 14461, 14479, 14489, 14503, 14519, 14533, 14543, + 14549, 14557, 14563, 14591, 14621, 14627, 14633, + 14639, 14653, 14669, 14683, 14699, 14713, 14723, + 14731, 14737, 14747, 14753, 14759, 14767, 14779, + 14797, 14813, 14821, 14827, 14843, 14851, 14867, + 14879, 14887, 14897, 14923, 14929, 14939, 14947, + 14957, 14969, 14983, 15013, 15031, 15053, 15061, + 15073, 15083, 15091, 15101, 15107, 15121, 15131, + 15137, 15149, 15161, 15173, 15187, 15193, 15199, + 15217, 15227, 15233, 15241, 15259, 15269, 15277, + 15287, 15299, 15307, 15313, 15319, 15329, 15349, + 15359, 15373, 15383, 15391, 15401, 15413, 15427, + 15439, 15451, 15461, 15467, 15473, 15493, 15511, + 15527, 15541, 15551, 15559, 15569, 15581, 15601, + 15607, 15619, 15629, 15641, 15647, 15661, 15667, + 15679, 15727, 15733, 15739, 15749, 15761, 15767, + 15773, 15787, 15797, 15803, 15809, 15817, 15823, + 15859, 15877, 15887, 15901, 15907, 15913, 15919, + 15937, 15959, 15971, 15991, 16001, 16007, 16033, + 16057, 16063, 16069, 16087, 16097, 16103, 16111, + 16127, 16139, 16183, 16189, 16217, 16223, 16229, + 16249, 16267, 16273, 16301, 16319, 16333, 16339, + 16349, 16361, 16369, 16381, 16411, 16417, 16427, + 16433, 16447, 16453, 16477, 16487, 16493, 16519, + 16529, 16547, 16553, 16561, 16567, 16573, 16603, + 16619, 16631, 16649, 16657, 16673, 16691, 16699, + 16729, 16741, 16747, 16759, 16787, 16811, 16823, + 16829, 16843, 16871, 16879, 16889, 16901, 16921, + 16927, 16937, 16943, 16963, 16979, 16987, 16993, + 17011, 17021, 17027, 17033, 17041, 17047, 17053, + 17077, 17093, 17099, 17107, 17117, 17123, 17137, + 17159, 17167, 17183, 17189, 17203, 17209, 17231, + 17239, 17257, 17291, 17299, 17317, 17327, 17333, + 17341, 17351, 17359, 17377, 17383, 17389, 17401, + 17417, 17431, 17443, 17449, 17467, 17477, 17483, + 17489, 17497, 17509, 17519, 17539, 17551, 17569, + 17579, 17597, 17609, 17623, 17657, 17669, 17681, + 17707, 17713, 17729, 17737, 17747, 17761, 17783, + 17789, 17807, 17827, 17837, 17851, 17863, 17881, + 17891, 17903, 17909, 17921, 17929, 17939, 17957, + 17971, 17977, 17987, 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17175922903, 17176053997, 17176185083, 17176316167, + 17176447243, 17176578343, 17176709449, 17176840529, 17176971601, 17177102693, 17177233783, + 17177364857, 17177495953, 17177627053, 17177758133}; + +/** + * @brief Indicates whether the input `num` is a prime number. + * + * @param num + * @return A boolean indicating whether the input `num` is a prime number + */ +constexpr bool is_prime(std::size_t num) noexcept +{ + bool flag = true; + // 0 and 1 are not prime numbers + if (num == 0lu || num == 1lu) { + flag = false; + } else { + for (auto i = 2lu; i <= num / 2lu; ++i) { + if (num % i == 0) { + flag = false; + break; + } + } + } + return flag; +} + +/** + * @brief Computes the smallest prime number greater than or equal to `num`. + * + * @param num + * @return The smallest prime number greater than or equal to `num` + */ +constexpr std::size_t compute_prime(std::size_t num) noexcept +{ + while (not is_prime(num)) { + num++; + } + return num; +} + +/** + * @brief Calculates the valid capacity based on `cg_size` , `vector_width` + * and the initial `capacity`. + * + * @tparam cg_size Cooperative Group size + * @tparam vector_width Length of vector load + * @tparam uses_vector_load If vector load is used + * + * @param capacity The initially requested capacity + * @return A valid capacity no smaller than the requested `capacity` + */ +template +constexpr std::size_t get_valid_capacity(std::size_t capacity) noexcept +{ + auto const stride = [&]() { + if constexpr (uses_vector_load) { return cg_size * vector_width; } + if constexpr (not uses_vector_load) { return cg_size; } + }(); + + auto const c = SDIV(capacity, stride); + auto const min_prime = std::lower_bound(primes.begin(), primes.end(), c); + return *min_prime * stride; +} + +} // namespace detail +} // namespace cuco diff --git a/include/cuco/detail/probe_sequence_impl.cuh b/include/cuco/detail/probe_sequence_impl.cuh new file mode 100644 index 000000000..3145e8fa5 --- /dev/null +++ b/include/cuco/detail/probe_sequence_impl.cuh @@ -0,0 +1,380 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +#include +#include + +namespace cuco { +namespace detail { + +/** + * @brief Base class of public probe sequence. This class should not be used directly. + * + * @tparam CGSize Size of CUDA Cooperative Groups + */ +template +class probe_sequence_base { + protected: + /** + * @brief Returns the size of the CUDA cooperative thread group. + */ + static constexpr std::size_t cg_size = CGSize; + + /** + * @brief Returns the number of elements loaded with each vector load. + */ + static constexpr uint32_t vector_width() noexcept { return 2u; } +}; + +/* + * @brief Base class of probe sequence implementation. + * + * Hash map operations are generally memory-bandwidth bound. A vector-load loads two consecutive + * slots instead of one to fully utilize the 16B memory load supported by SASS/hardware thus + * improve memory throughput. This method (flagged by `uses_vector_load` logic) is implicitly + * applied to all hash map operations (e.g. `insert`, `count`, and `retrieve`, etc.) when pairs + * are packable (see `cuco::detail::is_packable` logic). + * + * @tparam Key Type used for keys + * @tparam Value Type of the mapped values + * @tparam Scope The scope in which multimap operations will be performed by + * individual threads + * @tparam VectorWidth Length of vector load + * @tparam CGSize Size of CUDA Cooperative Groups + */ +template +class probe_sequence_impl_base { + protected: + using value_type = cuco::pair_type; + using key_type = Key; + using mapped_type = Value; + using atomic_key_type = cuda::atomic; + using atomic_mapped_type = cuda::atomic; + using pair_atomic_type = cuco::pair_type; + using iterator = pair_atomic_type*; + using const_iterator = pair_atomic_type const*; + + /** + * @brief Returns the number of elements loaded with each vector-load. + */ + static constexpr uint32_t vector_width = VectorWidth; + + /** + * @brief Returns the size of the CUDA cooperative thread group. + */ + static constexpr std::size_t cg_size = CGSize; + + /** + * @brief Indicates if vector-load is used. + * + * Users have no explicit control on whether vector-load is used. + * + * @return Boolean indicating if vector-load is used. + */ + __host__ __device__ static constexpr bool uses_vector_load() noexcept + { + return cuco::detail::is_packable(); + } + + /** + * @brief Constructs a probe sequence based on the given hash map features. + * + * @param slots Pointer to beginning of the hash map slots + * @param capacity Capacity of the hash map + */ + __host__ __device__ explicit probe_sequence_impl_base(iterator slots, std::size_t capacity) + : slots_{slots}, capacity_{capacity} + { + } + + /** + * @brief Returns the capacity of the hash map. + */ + __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept + { + return capacity_; + } + + /** + * @brief Returns slots array. + */ + __device__ __forceinline__ iterator get_slots() noexcept { return slots_; } + + /** + * @brief Returns slots array. + */ + __device__ __forceinline__ const_iterator get_slots() const noexcept { return slots_; } + + iterator slots_; ///< Pointer to beginning of the hash map slots + const std::size_t capacity_; ///< Total number of slots +}; // class probe_sequence_impl_base + +/** + * @brief Cooperative Groups based Linear probing scheme. + * + * @tparam Key Type used for keys + * @tparam Value Type of the mapped values + * @tparam Scope The scope in which multimap operations will be performed by + * individual threads + * @tparam VectorWidth Length of vector load + * @tparam CGSize Size of CUDA Cooperative Groups + * @tparam Hash Unary callable type + */ +template +class linear_probing_impl + : public probe_sequence_impl_base { + public: + using probe_sequence_impl_base_type = + probe_sequence_impl_base; + using value_type = typename probe_sequence_impl_base_type::value_type; + using key_type = typename probe_sequence_impl_base_type::key_type; + using mapped_type = typename probe_sequence_impl_base_type::mapped_type; + using atomic_key_type = typename probe_sequence_impl_base_type::atomic_key_type; + using atomic_mapped_type = typename probe_sequence_impl_base_type::atomic_mapped_type; + using pair_atomic_type = typename probe_sequence_impl_base_type::pair_atomic_type; + using iterator = typename probe_sequence_impl_base_type::iterator; + using const_iterator = typename probe_sequence_impl_base_type::const_iterator; + + using probe_sequence_impl_base_type::capacity_; + using probe_sequence_impl_base_type::cg_size; + using probe_sequence_impl_base_type::slots_; + using probe_sequence_impl_base_type::uses_vector_load; + using probe_sequence_impl_base_type::vector_width; + + /** + * @brief Constructs a linear probing scheme based on the given hash map features. + * + * @param slots Pointer to beginning of the hash map slots + * @param capacity Capacity of the hash map + * @param hash Unary function to hash each key + */ + __host__ __device__ explicit linear_probing_impl(iterator slots, std::size_t capacity) + : probe_sequence_impl_base_type{slots, capacity}, hash_{Hash{}} + { + } + + /** + * @brief Returns the initial slot for a given key `k`. + * + * If vector-load is enabled, the return slot is always even to avoid illegal memory access. + * + * @tparam CG CUDA Cooperative Groups type + * @param g the Cooperative Group for which the initial slot is needed + * @param k The key to get the slot for + * @return Pointer to the initial slot for `k` + */ + template + __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept + { + auto const hash_value = [&]() { + auto const tmp = hash_(k); + if constexpr (uses_vector_load()) { + // initial hash value is always even + return tmp + tmp % 2; + } + if constexpr (not uses_vector_load()) { return tmp; } + }(); + + auto const offset = [&]() { + if constexpr (uses_vector_load()) { return g.thread_rank() * vector_width; } + if constexpr (not uses_vector_load()) { return g.thread_rank(); } + }(); + + // Each CG accesses to a window of (`cg_size` * `vector_width`) + // slots if vector-load is used or `cg_size` slots otherwise + return &slots_[(hash_value + offset) % capacity_]; + } + + /** + * @brief Given a slot `s`, returns the next slot. + * + * If `s` is the last slot, wraps back around to the first slot. + * + * @param s The slot to advance + * @return The next slot after `s` + */ + __device__ __forceinline__ iterator next_slot(iterator s) noexcept + { + std::size_t index = s - slots_; + std::size_t offset; + if constexpr (uses_vector_load()) { + offset = cg_size * vector_width; + } else { + offset = cg_size; + } + return &slots_[(index + offset) % capacity_]; + } + + private: + Hash hash_; ///< The unary callable used to hash the key +}; // class linear_probing + +/** + * @brief Cooperative Groups based double hashing scheme. + * + * Default probe sequence for `cuco::static_multimap`. Double hashing shows superior + * performance when dealing with high multiplicty and/or high occupancy use cases. Performance + * hints: + * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise. + * + * `Hash1` and `Hash2` should be callable object type. + * + * @tparam Key Type used for keys + * @tparam Value Type of the mapped values + * @tparam Scope The scope in which multimap operations will be performed by + * individual threads + * @tparam VectorWidth Length of vector load + * @tparam CGSize Size of CUDA Cooperative Groups + * @tparam Hash1 Unary callable type + * @tparam Hash2 Unary callable type + */ +template +class double_hashing_impl + : public probe_sequence_impl_base { + public: + using probe_sequence_impl_base_type = + probe_sequence_impl_base; + using value_type = typename probe_sequence_impl_base_type::value_type; + using key_type = typename probe_sequence_impl_base_type::key_type; + using mapped_type = typename probe_sequence_impl_base_type::mapped_type; + using atomic_key_type = typename probe_sequence_impl_base_type::atomic_key_type; + using atomic_mapped_type = typename probe_sequence_impl_base_type::atomic_mapped_type; + using pair_atomic_type = typename probe_sequence_impl_base_type::pair_atomic_type; + using iterator = typename probe_sequence_impl_base_type::iterator; + using const_iterator = typename probe_sequence_impl_base_type::const_iterator; + + using probe_sequence_impl_base_type::capacity_; + using probe_sequence_impl_base_type::cg_size; + using probe_sequence_impl_base_type::slots_; + using probe_sequence_impl_base_type::uses_vector_load; + using probe_sequence_impl_base_type::vector_width; + + /** + * @brief Constructs a double hashing scheme based on the given hash map features. + * + * `hash2` takes a different seed to reduce the chance of secondary clustering. + * + * @param slots Pointer to beginning of the hash map slots + * @param capacity Capacity of the hash map + * @param hash1 First hasher to hash each key + * @param hash2 Second hasher to determine step size + */ + __host__ __device__ explicit double_hashing_impl(iterator slots, std::size_t capacity) + : probe_sequence_impl_base_type{slots, capacity}, + hash1_{Hash1{}}, + hash2_{Hash2{1}}, + step_size_{} + { + } + + /** + * @brief Returns the initial slot for a given key `k`. + * + * If vector-load is enabled, the return slot is always a multiple of (`cg_size` * `vector_width`) + * to avoid illegal memory access. + * + * @tparam CG CUDA Cooperative Groups type + * @param g the Cooperative Group for which the initial slot is needed + * @param k The key to get the slot for + * @return Pointer to the initial slot for `k` + */ + template + __device__ __forceinline__ iterator initial_slot(CG const& g, Key const k) noexcept + { + std::size_t index; + auto const hash_value = hash1_(k); + if constexpr (uses_vector_load()) { + // step size in range [1, prime - 1] * cg_size * vector_width + step_size_ = + (hash2_(k) % (capacity_ / (cg_size * vector_width) - 1) + 1) * cg_size * vector_width; + index = hash_value % (capacity_ / (cg_size * vector_width)) * cg_size * vector_width + + g.thread_rank() * vector_width; + } else { + // step size in range [1, prime - 1] * cg_size + step_size_ = (hash2_(k) % (capacity_ / cg_size - 1) + 1) * cg_size; + index = (hash_value + g.thread_rank()) % capacity_; + } + return slots_ + index; + } + + /** + * @brief Given a slot `s`, returns the next slot. + * + * If `s` is the last slot, wraps back around to the first slot. + * + * @param s The slot to advance + * @return The next slot after `s` + */ + __device__ __forceinline__ iterator next_slot(iterator s) noexcept + { + std::size_t index = s - slots_; + return &slots_[(index + step_size_) % capacity_]; + } + + private: + Hash1 hash1_; ///< The first unary callable used to hash the key + Hash2 hash2_; ///< The second unary callable used to determine step size + std::size_t step_size_; ///< The step stride when searching for the next slot +}; // class double_hashing + +/** + * @brief Probe sequence used internally by hash map. + * + * @tparam ProbeImpl Type of probe sequence implementation + * @tparam Key Type used for keys + * @tparam Value Type of the mapped values + * @tparam Scope The scope in which multimap operations will be performed by + * individual threads + */ +template +class probe_sequence : public ProbeImpl::template impl { + public: + using impl_type = typename ProbeImpl::template impl; + + /** + * @brief Constructs a probe sequence based on the given hash map features. + * + * @param slots Pointer to beginning of the hash map slots + * @param capacity Capacity of the hash map + */ + __host__ __device__ explicit probe_sequence(typename impl_type::iterator slots, + std::size_t capacity) + : impl_type{slots, capacity} + { + } +}; // class probe_sequence + +} // namespace detail +} // namespace cuco diff --git a/include/cuco/detail/static_map_kernels.cuh b/include/cuco/detail/static_map_kernels.cuh index aa4606aa6..e166de3c6 100644 --- a/include/cuco/detail/static_map_kernels.cuh +++ b/include/cuco/detail/static_map_kernels.cuh @@ -12,9 +12,9 @@ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. - */ + */ -namespace cuco{ +namespace cuco { namespace detail { namespace cg = cooperative_groups; @@ -23,7 +23,7 @@ namespace cg = cooperative_groups; * * Each space in `slots` that can hold a key value pair is initialized to a * `pair_atomic_type` containing the key `k` and the value `v`. - * + * * @tparam atomic_key_type Type of the `Key` atomic container * @tparam atomic_mapped_type Type of the `Value` atomic container * @tparam Key key type @@ -34,16 +34,14 @@ namespace cg = cooperative_groups; * @param v Value to which all values in `slots` are initialized * @param size Size of the storage pointed to by `slots` */ -template -__global__ void initialize( - pair_atomic_type* const slots, Key k, - Value v, std::size_t size) { - +template +__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size) +{ auto tid = block_size * blockIdx.x + threadIdx.x; while (tid < size) { new (&slots[tid].first) atomic_key_type{k}; @@ -53,14 +51,14 @@ __global__ void initialize( } /** - * @brief Inserts all key/value pairs in the range `[first, last)`. - * + * @brief Inserts all key/value pairs in the range `[first, last)`. + * * If multiple keys in `[first, last)` compare equal, it is unspecified which * element is inserted. - * - * @tparam block_size + * + * @tparam block_size * @tparam InputIt Device accessible input iterator whose `value_type` is -* convertible to the map's `value_type` + * convertible to the map's `value_type` * @tparam atomicT Type of atomic storage * @tparam viewT Type of device view allowing access of hash map storage * @tparam Hash Unary callable type @@ -72,25 +70,22 @@ __global__ void initialize( * @param hash The unary function to apply to hash each key * @param key_equal The binary function used to compare two keys for equality */ -template -__global__ void insert(InputIt first, - InputIt last, - atomicT* num_successes, - viewT view, - Hash hash, - KeyEqual key_equal) { +template +__global__ void insert( + InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal) +{ typedef cub::BlockReduce BlockReduce; __shared__ typename BlockReduce::TempStorage temp_storage; std::size_t thread_num_successes = 0; - + auto tid = block_size * blockIdx.x + threadIdx.x; - auto it = first + tid; - + auto it = first + tid; + while (it < last) { typename viewT::value_type const insert_pair{*it}; if (view.insert(insert_pair, hash, key_equal)) { thread_num_successes++; } @@ -100,25 +95,23 @@ __global__ void insert(InputIt first, // compute number of successfully inserted elements for each block // and atomically add to the grand total std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes); - if(threadIdx.x == 0) { - *num_successes += block_num_successes; - } + if (threadIdx.x == 0) { *num_successes += block_num_successes; } } /** - * @brief Inserts all key/value pairs in the range `[first, last)`. - * + * @brief Inserts all key/value pairs in the range `[first, last)`. + * * If multiple keys in `[first, last)` compare equal, it is unspecified which * element is inserted. Uses the CUDA Cooperative Groups API to leverage groups - * of multiple threads to perform each key/value insertion. This provides a + * of multiple threads to perform each key/value insertion. This provides a * significant boost in throughput compared to the non Cooperative Group * `insert` at moderate to high load factors. - * - * @tparam block_size + * + * @tparam block_size * @tparam tile_size The number of threads in the Cooperative Groups used to perform * inserts * @tparam InputIt Device accessible input iterator whose `value_type` is -* convertible to the map's `value_type` + * convertible to the map's `value_type` * @tparam atomicT Type of atomic storage * @tparam viewT Type of device view allowing access of hash map storage * @tparam Hash Unary callable type @@ -130,27 +123,24 @@ __global__ void insert(InputIt first, * @param hash The unary function to apply to hash each key * @param key_equal The binary function used to compare two keys for equality */ -template -__global__ void insert(InputIt first, - InputIt last, - atomicT* num_successes, - viewT view, - Hash hash, - KeyEqual key_equal) { +template +__global__ void insert( + InputIt first, InputIt last, atomicT* num_successes, viewT view, Hash hash, KeyEqual key_equal) +{ typedef cub::BlockReduce BlockReduce; __shared__ typename BlockReduce::TempStorage temp_storage; std::size_t thread_num_successes = 0; auto tile = cg::tiled_partition(cg::this_thread_block()); - auto tid = block_size * blockIdx.x + threadIdx.x; - auto it = first + tid / tile_size; - + auto tid = block_size * blockIdx.x + threadIdx.x; + auto it = first + tid / tile_size; + while (it < last) { // force conversion to value_type typename viewT::value_type const insert_pair{*it}; @@ -159,25 +149,23 @@ __global__ void insert(InputIt first, } it += (gridDim.x * block_size) / tile_size; } - + // compute number of successfully inserted elements for each block // and atomically add to the grand total std::size_t block_num_successes = BlockReduce(temp_storage).Sum(thread_num_successes); - if(threadIdx.x == 0) { - *num_successes += block_num_successes; - } + if (threadIdx.x == 0) { *num_successes += block_num_successes; } } /** * @brief Finds the values corresponding to all keys in the range `[first, last)`. - * - * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. - * Else, copies the empty value sentinel. + * + * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. + * Else, copies the empty value sentinel. * @tparam block_size The size of the thread block * @tparam Value The type of the mapped value for the map * @tparam InputIt Device accessible input iterator whose `value_type` is * convertible to the map's `key_type` - * @tparam OutputIt Device accessible output iterator whose `value_type` is + * @tparam OutputIt Device accessible output iterator whose `value_type` is * convertible to the map's `mapped_type` * @tparam viewT Type of device view allowing access of hash map storage * @tparam Hash Unary callable type @@ -189,37 +177,34 @@ __global__ void insert(InputIt first, * @param hash The unary function to apply to hash each key * @param key_equal The binary function to compare two keys for equality */ -template -__global__ void find(InputIt first, - InputIt last, - OutputIt output_begin, - viewT view, - Hash hash, - KeyEqual key_equal) { - auto tid = block_size * blockIdx.x + threadIdx.x; +template +__global__ void find( + InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal) +{ + auto tid = block_size * blockIdx.x + threadIdx.x; auto key_idx = tid; __shared__ Value writeBuffer[block_size]; - - while(first + key_idx < last) { - auto key = *(first + key_idx); + + while (first + key_idx < last) { + auto key = *(first + key_idx); auto found = view.find(key, hash, key_equal); - /* - * The ld.relaxed.gpu instruction used in view.find causes L1 to + /* + * The ld.relaxed.gpu instruction used in view.find causes L1 to * flush more frequently, causing increased sector stores from L2 to global memory. * By writing results to shared memory and then synchronizing before writing back * to global, we no longer rely on L1, preventing the increase in sector stores from * L2 to global and improving performance. */ - writeBuffer[threadIdx.x] = - found == view.end() - ? view.get_empty_value_sentinel() - : found->second.load(cuda::std::memory_order_relaxed); + writeBuffer[threadIdx.x] = found == view.end() + ? view.get_empty_value_sentinel() + : found->second.load(cuda::std::memory_order_relaxed); __syncthreads(); *(output_begin + key_idx) = writeBuffer[threadIdx.x]; key_idx += gridDim.x * block_size; @@ -228,10 +213,10 @@ __global__ void find(InputIt first, /** * @brief Finds the values corresponding to all keys in the range `[first, last)`. - * - * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. + * + * If the key `*(first + i)` exists in the map, copies its associated value to `(output_begin + i)`. * Else, copies the empty value sentinel. Uses the CUDA Cooperative Groups API to leverage groups - * of multiple threads to find each key. This provides a significant boost in throughput compared + * of multiple threads to find each key. This provides a significant boost in throughput compared * to the non Cooperative Group `find` at moderate to high load factors. * * @tparam block_size The size of the thread block @@ -240,7 +225,7 @@ __global__ void find(InputIt first, * @tparam Value The type of the mapped value for the map * @tparam InputIt Device accessible input iterator whose `value_type` is * convertible to the map's `key_type` - * @tparam OutputIt Device accessible output iterator whose `value_type` is + * @tparam OutputIt Device accessible output iterator whose `value_type` is * convertible to the map's `mapped_type` * @tparam viewT Type of device view allowing access of hash map storage * @tparam Hash Unary callable type @@ -252,42 +237,40 @@ __global__ void find(InputIt first, * @param hash The unary function to apply to hash each key * @param key_equal The binary function to compare two keys for equality */ -template -__global__ void find(InputIt first, - InputIt last, - OutputIt output_begin, - viewT view, - Hash hash, - KeyEqual key_equal) { - auto tile = cg::tiled_partition(cg::this_thread_block()); - auto tid = block_size * blockIdx.x + threadIdx.x; +template +__global__ void find( + InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; auto key_idx = tid / tile_size; __shared__ Value writeBuffer[block_size]; - - while(first + key_idx < last) { - auto key = *(first + key_idx); + + while (first + key_idx < last) { + auto key = *(first + key_idx); auto found = view.find(tile, key, hash, key_equal); - - /* - * The ld.relaxed.gpu instruction used in view.find causes L1 to + + /* + * The ld.relaxed.gpu instruction used in view.find causes L1 to * flush more frequently, causing increased sector stores from L2 to global memory. * By writing results to shared memory and then synchronizing before writing back * to global, we no longer rely on L1, preventing the increase in sector stores from * L2 to global and improving performance. */ - if(tile.thread_rank() == 0) { + if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = - found == view.end() - ? view.get_empty_value_sentinel() - : found->second.load(cuda::std::memory_order_relaxed); + found == view.end() ? view.get_empty_value_sentinel() + : found->second.load(cuda::std::memory_order_relaxed); } __syncthreads(); - if(tile.thread_rank() == 0) { + if (tile.thread_rank() == 0) { *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size]; } key_idx += (gridDim.x * block_size) / tile_size; @@ -296,7 +279,7 @@ __global__ void find(InputIt first, /** * @brief Indicates whether the keys in the range `[first, last)` are contained in the map. - * + * * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map. * * @tparam block_size The size of the thread block @@ -306,7 +289,7 @@ __global__ void find(InputIt first, * convertible to the map's `mapped_type` * @tparam viewT Type of device view allowing access of hash map storage * @tparam Hash Unary callable type - * @tparam KeyEqual Binary callable type + * @tparam KeyEqual Binary callable type * @param first Beginning of the sequence of keys * @param last End of the sequence of keys * @param output_begin Beginning of the sequence of booleans for the presence of each key @@ -314,26 +297,24 @@ __global__ void find(InputIt first, * @param hash The unary function to apply to hash each key * @param key_equal The binary function to compare two keys for equality */ -template -__global__ void contains(InputIt first, - InputIt last, - OutputIt output_begin, - viewT view, - Hash hash, - KeyEqual key_equal) { - auto tid = block_size * blockIdx.x + threadIdx.x; +template +__global__ void contains( + InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal) +{ + auto tid = block_size * blockIdx.x + threadIdx.x; auto key_idx = tid; __shared__ bool writeBuffer[block_size]; - - while(first + key_idx < last) { + + while (first + key_idx < last) { auto key = *(first + key_idx); - - /* - * The ld.relaxed.gpu instruction used in view.find causes L1 to + + /* + * The ld.relaxed.gpu instruction used in view.find causes L1 to * flush more frequently, causing increased sector stores from L2 to global memory. * By writing results to shared memory and then synchronizing before writing back * to global, we no longer rely on L1, preventing the increase in sector stores from @@ -348,10 +329,10 @@ __global__ void contains(InputIt first, /** * @brief Indicates whether the keys in the range `[first, last)` are contained in the map. - * + * * Writes a `bool` to `(output + i)` indicating if the key `*(first + i)` exists in the map. - * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the - * contains operation for each key. This provides a significant boost in throughput compared + * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the + * contains operation for each key. This provides a significant boost in throughput compared * to the non Cooperative Group `contains` at moderate to high load factors. * * @tparam block_size The size of the thread block @@ -363,7 +344,7 @@ __global__ void contains(InputIt first, * convertible to the map's `mapped_type` * @tparam viewT Type of device view allowing access of hash map storage * @tparam Hash Unary callable type - * @tparam KeyEqual Binary callable type + * @tparam KeyEqual Binary callable type * @param first Beginning of the sequence of keys * @param last End of the sequence of keys * @param output_begin Beginning of the sequence of booleans for the presence of each key @@ -371,43 +352,40 @@ __global__ void contains(InputIt first, * @param hash The unary function to apply to hash each key * @param key_equal The binary function to compare two keys for equality */ -template -__global__ void contains(InputIt first, - InputIt last, - OutputIt output_begin, - viewT view, - Hash hash, - KeyEqual key_equal) { - auto tile = cg::tiled_partition(cg::this_thread_block()); - auto tid = block_size * blockIdx.x + threadIdx.x; +template +__global__ void contains( + InputIt first, InputIt last, OutputIt output_begin, viewT view, Hash hash, KeyEqual key_equal) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; auto key_idx = tid / tile_size; __shared__ bool writeBuffer[block_size]; - - while(first + key_idx < last) { - auto key = *(first + key_idx); + + while (first + key_idx < last) { + auto key = *(first + key_idx); auto found = view.contains(tile, key, hash, key_equal); - - /* - * The ld.relaxed.gpu instruction used in view.find causes L1 to + + /* + * The ld.relaxed.gpu instruction used in view.find causes L1 to * flush more frequently, causing increased sector stores from L2 to global memory. * By writing results to shared memory and then synchronizing before writing back * to global, we no longer rely on L1, preventing the increase in sector stores from * L2 to global and improving performance. */ - if(tile.thread_rank() == 0) { - writeBuffer[threadIdx.x / tile_size] = found; - } + if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; } __syncthreads(); - if(tile.thread_rank() == 0) { + if (tile.thread_rank() == 0) { *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size]; } key_idx += (gridDim.x * block_size) / tile_size; } } -} // namespace detail -} // namespace cuco +} // namespace detail +} // namespace cuco diff --git a/include/cuco/detail/static_multimap/device_view_impl.inl b/include/cuco/detail/static_multimap/device_view_impl.inl new file mode 100644 index 000000000..c4e520446 --- /dev/null +++ b/include/cuco/detail/static_multimap/device_view_impl.inl @@ -0,0 +1,1296 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +namespace cuco { + +template +class static_multimap::device_view_impl_base { + protected: + // Import member type definitions from `static_multimap` + using value_type = value_type; + using key_type = Key; + using mapped_type = Value; + using iterator = pair_atomic_type*; + using const_iterator = pair_atomic_type const*; + using probe_sequence_type = probe_sequence_type; + + /** + * @brief Indicates if vector-load is used. + * + * Users have no explicit control on whether vector-load is used. + * + * @return Boolean indicating if vector-load is used. + */ + static constexpr bool uses_vector_load() noexcept + { + return probe_sequence_type::uses_vector_load(); + } + + /** + * @brief Returns the number of pairs loaded with each vector-load + */ + static constexpr uint32_t vector_width() noexcept { return probe_sequence_type::vector_width(); } + + __host__ __device__ device_view_impl_base(pair_atomic_type* slots, + std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel) noexcept + : probe_sequence_{slots, capacity}, + empty_key_sentinel_{empty_key_sentinel}, + empty_value_sentinel_{empty_value_sentinel} + { + } + + /** + * @brief Gets slots array. + * + * @return Slots array + */ + __device__ __forceinline__ pair_atomic_type* get_slots() noexcept + { + return probe_sequence_.get_slots(); + } + + /** + * @brief Gets slots array. + * + * @return Slots array + */ + __device__ __forceinline__ pair_atomic_type const* get_slots() const noexcept + { + return probe_sequence_.get_slots(); + } + + /** + * @brief Returns the initial slot for a given key `k` + * + * To be used for Cooperative Group based probing. + * + * @tparam CG Cooperative Group type + * @param g the Cooperative Group for which the initial slot is needed + * @param k The key to get the slot for + * @return Pointer to the initial slot for `k` + */ + template + __device__ __forceinline__ iterator initial_slot(CG const& g, Key const& k) noexcept + { + return probe_sequence_.initial_slot(g, k); + } + + /** + * @brief Returns the initial slot for a given key `k` + * + * To be used for Cooperative Group based probing. + * + * @tparam CG Cooperative Group type + * @param g the Cooperative Group for which the initial slot is needed + * @param k The key to get the slot for + * @return Pointer to the initial slot for `k` + */ + template + __device__ __forceinline__ const_iterator initial_slot(CG g, Key const& k) const noexcept + { + return probe_sequence_.initial_slot(g, k); + } + + /** + * @brief Given a slot `s`, returns the next slot. + * + * If `s` is the last slot, wraps back around to the first slot. To + * be used for Cooperative Group based probing. + * + * @param s The slot to advance + * @return The next slot after `s` + */ + __device__ __forceinline__ iterator next_slot(iterator s) noexcept + { + return probe_sequence_.next_slot(s); + } + + /** + * @brief Given a slot `s`, returns the next slot. + * + * If `s` is the last slot, wraps back around to the first slot. To + * be used for Cooperative Group based probing. + * + * @param s The slot to advance + * @return The next slot after `s` + */ + __device__ __forceinline__ const_iterator next_slot(const_iterator s) const noexcept + { + return probe_sequence_.next_slot(s); + } + + /** + * @brief Gets the maximum number of elements the hash map can hold. + * + * @return The maximum number of elements the hash map can hold + */ + __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept + { + return probe_sequence_.get_capacity(); + } + + /** + * @brief Gets the sentinel value used to represent an empty key slot. + * + * @return The sentinel value used to represent an empty key slot + */ + __host__ __device__ __forceinline__ Key get_empty_key_sentinel() const noexcept + { + return empty_key_sentinel_; + } + + /** + * @brief Gets the sentinel value used to represent an empty value slot. + * + * @return The sentinel value used to represent an empty value slot + */ + __host__ __device__ __forceinline__ Value get_empty_value_sentinel() const noexcept + { + return empty_value_sentinel_; + } + + /** + * @brief Load two key/value pairs from the given slot to the target pair array. + * + * @param arr The pair array to be loaded + * @param current_slot The given slot to load from + */ + __device__ __forceinline__ void load_pair_array(value_type* arr, + const_iterator current_slot) noexcept + { + if constexpr (sizeof(value_type) == 4) { + auto const tmp = *reinterpret_cast(current_slot); + memcpy(&arr[0], &tmp, 2 * sizeof(value_type)); + } else { + auto const tmp = *reinterpret_cast(current_slot); + memcpy(&arr[0], &tmp, 2 * sizeof(value_type)); + } + } + + private: + probe_sequence_type probe_sequence_; ///< Probe sequence used to probe the hash map + Key empty_key_sentinel_{}; ///< Key value that represents an empty slot + Value empty_value_sentinel_{}; ///< Initial Value of empty slot +}; // class device_view_impl_base + +template +class static_multimap::device_mutable_view_impl + : public device_view_impl_base { + public: + using value_type = typename device_view_impl_base::value_type; + using key_type = typename device_view_impl_base::key_type; + using mapped_type = typename device_view_impl_base::mapped_type; + using iterator = typename device_view_impl_base::iterator; + using const_iterator = typename device_view_impl_base::const_iterator; + + private: + /** + * @brief Enumeration of the possible results of attempting to insert into a hash bucket. + */ + enum class insert_result { + CONTINUE, ///< Insert did not succeed, continue trying to insert + SUCCESS, ///< New pair inserted successfully + DUPLICATE ///< Insert did not succeed, key is already present + }; + + /** + * @brief Inserts the specified key/value pair with one single CAS operation. + * + * @param current_slot The slot to insert + * @param insert_pair The pair to insert + * @param key_equal The binary callable used to compare two keys for + * equality + * @return An insert result from the `insert_resullt` enumeration. + */ + __device__ __forceinline__ insert_result packed_cas(iterator current_slot, + value_type const& insert_pair) noexcept + { + auto expected_key = this->get_empty_key_sentinel(); + auto expected_value = this->get_empty_value_sentinel(); + + cuco::detail::pair_converter expected_pair{ + cuco::make_pair(std::move(expected_key), std::move(expected_value))}; + cuco::detail::pair_converter new_pair{insert_pair}; + + auto slot = reinterpret_cast< + cuda::atomic::packed_type, Scope>*>( + current_slot); + + bool success = slot->compare_exchange_strong( + expected_pair.packed, new_pair.packed, cuda::std::memory_order_relaxed); + if (success) { return insert_result::SUCCESS; } + + return insert_result::CONTINUE; + } + + /** + * @brief Inserts the specified key/value pair with two back-to-back CAS operations. + * + * @param current_slot The slot to insert + * @param insert_pair The pair to insert + * @return An insert result from the `insert_resullt` enumeration. + */ + __device__ __forceinline__ insert_result back_to_back_cas(iterator current_slot, + value_type const& insert_pair) noexcept + { + using cuda::std::memory_order_relaxed; + + auto expected_key = this->get_empty_key_sentinel(); + auto expected_value = this->get_empty_value_sentinel(); + + // Back-to-back CAS for 8B/8B key/value pairs + auto& slot_key = current_slot->first; + auto& slot_value = current_slot->second; + + bool key_success = + slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed); + bool value_success = + slot_value.compare_exchange_strong(expected_value, insert_pair.second, memory_order_relaxed); + + if (key_success) { + while (not value_success) { + value_success = + slot_value.compare_exchange_strong(expected_value = this->get_empty_value_sentinel(), + insert_pair.second, + memory_order_relaxed); + } + return insert_result::SUCCESS; + } else if (value_success) { + slot_value.store(this->get_empty_value_sentinel(), memory_order_relaxed); + } + + return insert_result::CONTINUE; + } + + /** + * @brief Inserts the specified key/value pair with a CAS of the key and a dependent write + * of the value. + * + * @param current_slot The slot to insert + * @param insert_pair The pair to insert + * @return An insert result from the `insert_resullt` enumeration. + */ + __device__ __forceinline__ insert_result + cas_dependent_write(iterator current_slot, value_type const& insert_pair) noexcept + { + using cuda::std::memory_order_relaxed; + auto expected_key = this->get_empty_key_sentinel(); + + auto& slot_key = current_slot->first; + + auto const key_success = + slot_key.compare_exchange_strong(expected_key, insert_pair.first, memory_order_relaxed); + + if (key_success) { + auto& slot_value = current_slot->second; + slot_value.store(insert_pair.second, memory_order_relaxed); + return insert_result::SUCCESS; + } + + return insert_result::CONTINUE; + } + + public: + __host__ __device__ device_mutable_view_impl(pair_atomic_type* slots, + std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel) noexcept + : device_view_impl_base{slots, capacity, empty_key_sentinel, empty_value_sentinel} + { + } + + /** + * @brief Inserts the specified key/value pair into the map using vector loads. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam CG Cooperative Group type + * + * @param g The Cooperative Group that performs the insert + * @param insert_pair The pair to insert + * @return void. + */ + template + __device__ __forceinline__ std::enable_if_t insert( + CG g, value_type const& insert_pair) noexcept + { + auto current_slot = initial_slot(g, insert_pair.first); + while (true) { + value_type arr[2]; + load_pair_array(&arr[0], current_slot); + + // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as + // the sentinel is not a valid key value. Therefore, first check for the sentinel + auto const first_slot_is_empty = + (detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel())); + auto const second_slot_is_empty = + (detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel())); + auto const window_contains_empty = g.ballot(first_slot_is_empty or second_slot_is_empty); + + if (window_contains_empty) { + // the first lane in the group with an empty slot will attempt the insert + insert_result status{insert_result::CONTINUE}; + uint32_t src_lane = __ffs(window_contains_empty) - 1; + if (g.thread_rank() == src_lane) { + auto insert_location = first_slot_is_empty ? current_slot : current_slot + 1; + // One single CAS operation since vector loads are dedicated to packable pairs + status = packed_cas(insert_location, insert_pair); + } + + // successful insert + if (g.any(status == insert_result::SUCCESS)) { return; } + // if we've gotten this far, a different key took our spot + // before we could insert. We need to retry the insert on the + // same window + } + // if there are no empty slots in the current window, + // we move onto the next window + else { + current_slot = next_slot(current_slot); + } + } // while true + } + + /** + * @brief Inserts the specified key/value pair into the map using scalar loads. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam CG Cooperative Group type + * + * @param g The Cooperative Group that performs the insert + * @param insert_pair The pair to insert + * @return void. + */ + template + __device__ __forceinline__ std::enable_if_t insert( + CG g, value_type const& insert_pair) noexcept + { + auto current_slot = initial_slot(g, insert_pair.first); + + while (true) { + value_type slot_contents = *reinterpret_cast(current_slot); + auto const& existing_key = slot_contents.first; + + // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as + // the sentinel is not a valid key value. Therefore, first check for the sentinel + auto const slot_is_empty = + detail::bitwise_compare(existing_key, this->get_empty_key_sentinel()); + auto const window_contains_empty = g.ballot(slot_is_empty); + + if (window_contains_empty) { + // the first lane in the group with an empty slot will attempt the insert + insert_result status{insert_result::CONTINUE}; + uint32_t src_lane = __ffs(window_contains_empty) - 1; + + if (g.thread_rank() == src_lane) { +#if __CUDA_ARCH__ < 700 + status = cas_dependent_write(current_slot, insert_pair); +#else + status = back_to_back_cas(current_slot, insert_pair); +#endif + } + + // successful insert + if (g.any(status == insert_result::SUCCESS)) { return; } + // if we've gotten this far, a different key took our spot + // before we could insert. We need to retry the insert on the + // same window + } + // if there are no empty slots in the current window, + // we move onto the next window + else { + current_slot = next_slot(current_slot); + } + } // while true + } +}; // class device_mutable_view_impl + +template +class static_multimap::device_view_impl + : public device_view_impl_base { + public: + using value_type = typename device_view_impl_base::value_type; + using key_type = typename device_view_impl_base::key_type; + using mapped_type = typename device_view_impl_base::mapped_type; + using iterator = typename device_view_impl_base::iterator; + using const_iterator = typename device_view_impl_base::const_iterator; + + __host__ __device__ device_view_impl(pair_atomic_type* slots, + std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel) noexcept + : device_view_impl_base{slots, capacity, empty_key_sentinel, empty_value_sentinel} + { + } + + /** + * @brief Flushes per-CG buffer into the output sequence. + * + * A given CUDA Cooperative Group, `g`, loads `num_outputs` key-value pairs from `output_buffer` + * and writes them into global memory in a coalesced fashion. CG-wide `memcpy_sync` is used if + * `CUCO_HAS_CG_MEMCPY_ASYNC` is defined and `thrust::is_contiguous_iterator_v` + * returns true. All threads of `g` must be active due to implicit CG-wide synchronization + * during flushing. + * + * @tparam CG Cooperative Group type + * @tparam atomicT Type of atomic storage + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @param g The Cooperative Group used to flush output buffer + * @param num_outputs Number of valid output in the buffer + * @param output_buffer Buffer of the key/value pair sequence + * @param num_matches Size of the output sequence + * @param output_begin Beginning of the output sequence of key/value pairs + */ + template + __device__ __forceinline__ void flush_output_buffer(CG const& g, + uint32_t const num_outputs, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin) noexcept + { + std::size_t offset; + const auto lane_id = g.thread_rank(); + if (0 == lane_id) { + offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed); + } + offset = g.shfl(offset, 0); + + if constexpr (thrust::is_contiguous_iterator_v) { +#if defined(CUCO_HAS_CG_MEMCPY_ASYNC) +#if defined(CUCO_HAS_CUDA_BARRIER) + cooperative_groups::memcpy_async( + g, + output_begin + offset, + output_buffer, + cuda::aligned_size_t(sizeof(value_type) * num_outputs)); +#else + cooperative_groups::memcpy_async( + g, output_begin + offset, output_buffer, sizeof(value_type) * num_outputs); +#endif // end CUCO_HAS_CUDA_BARRIER + return; +#endif // end CUCO_HAS_CG_MEMCPY_ASYNC + } + for (auto index = lane_id; index < num_outputs; index += g.size()) { + *(output_begin + offset + index) = output_buffer[index]; + } + } + + /** + * @brief Flushes per-CG buffer into the output sequences. + * + * A given CUDA Cooperative Group, `g`, loads `num_outputs` elements from `probe_output_buffer` + * and `num_outputs` elements from `contained_output_buffer`, then writes them into global + * memory started from `probe_output_begin` and `contained_output_begin` respectively. All + * threads of `g` must be active due to implicit CG-wide synchronization during flushing. + * + * @tparam CG Cooperative Group type + * @tparam atomicT Type of atomic storage + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @param g The Cooperative Group used to flush output buffer + * @param num_outputs Number of valid output in the buffer + * @param probe_output_buffer Buffer of the matched probe pair sequence + * @param contained_output_buffer Buffer of the matched contained pair sequence + * @param num_matches Size of the output sequence + * @param probe_output_begin Beginning of the output sequence of the matched probe pairs + * @param contained_output_begin Beginning of the output sequence of the matched contained + * pairs + */ + template + __device__ __forceinline__ void flush_output_buffer(CG const& g, + uint32_t const num_outputs, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin) noexcept + { + std::size_t offset; + const auto lane_id = g.thread_rank(); + if (0 == lane_id) { + offset = num_matches->fetch_add(num_outputs, cuda::std::memory_order_relaxed); + } + offset = g.shfl(offset, 0); + + for (auto index = lane_id; index < num_outputs; index += g.size()) { + auto& probe_pair = probe_output_buffer[index]; + auto& contained_pair = contained_output_buffer[index]; + thrust::get<0>(*(probe_output_begin + offset + index)) = probe_pair.first; + thrust::get<1>(*(probe_output_begin + offset + index)) = probe_pair.second; + thrust::get<0>(*(contained_output_begin + offset + index)) = contained_pair.first; + thrust::get<1>(*(contained_output_begin + offset + index)) = contained_pair.second; + } + } + + /** + * @brief Indicates whether the key `k` exists in the map using vector loads. + * + * If the key `k` was inserted into the map, `contains` returns + * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to + * to leverage multiple threads to perform a single `contains` operation. This provides a + * significant boost in throughput compared to the non Cooperative Group based + * `contains` at moderate to high load factors. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam CG Cooperative Group type + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the contains operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return A boolean indicating whether the key/value pair + * containing `k` was inserted + */ + template + __device__ __forceinline__ std::enable_if_t contains( + CG g, Key const& k, KeyEqual key_equal) noexcept + { + auto current_slot = initial_slot(g, k); + + while (true) { + value_type arr[2]; + load_pair_array(&arr[0], current_slot); + + auto const first_slot_is_empty = + detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()); + auto const second_slot_is_empty = + detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()); + auto const first_equals = (not first_slot_is_empty and key_equal(arr[0].first, k)); + auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k)); + + // the key we were searching for was found by one of the threads, so we return true + if (g.any(first_equals or second_equals)) { return true; } + + // we found an empty slot, meaning that the key we're searching for isn't present + if (g.any(first_slot_is_empty or second_slot_is_empty)) { return false; } + + // otherwise, all slots in the current window are full with other keys, so we move onto the + // next window + current_slot = next_slot(current_slot); + } + } + + /** + * @brief Indicates whether the key `k` exists in the map using scalar loads. + * + * If the key `k` was inserted into the map, `contains` returns + * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to + * to leverage multiple threads to perform a single `contains` operation. This provides a + * significant boost in throughput compared to the non Cooperative Group + * `contains` at moderate to high load factors. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam CG Cooperative Group type + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the contains operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return A boolean indicating whether the key/value pair + * containing `k` was inserted + */ + template + __device__ __forceinline__ std::enable_if_t contains( + CG g, Key const& k, KeyEqual key_equal) noexcept + { + auto current_slot = initial_slot(g, k); + + while (true) { + value_type slot_contents = *reinterpret_cast(current_slot); + auto const& existing_key = slot_contents.first; + + // The user provide `key_equal` can never be used to compare against `empty_key_sentinel` as + // the sentinel is not a valid key value. Therefore, first check for the sentinel + auto const slot_is_empty = + detail::bitwise_compare(existing_key, this->get_empty_key_sentinel()); + + auto const equals = (not slot_is_empty and key_equal(existing_key, k)); + + // the key we were searching for was found by one of the threads, so we return true + if (g.any(equals)) { return true; } + + // we found an empty slot, meaning that the key we're searching for isn't present + if (g.any(slot_is_empty)) { return false; } + + // otherwise, all slots in the current window are full with other keys, so we move onto the + // next window + current_slot = next_slot(current_slot); + } + } + + /** + * @brief Counts the occurrence of a given key contained in multimap using vector loads. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam CG Cooperative Group type + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the count operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return Number of matches found by the current thread + */ + template + __device__ __forceinline__ std::enable_if_t count( + CG const& g, Key const& k, KeyEqual key_equal) noexcept + { + std::size_t count = 0; + auto current_slot = initial_slot(g, k); + + [[maybe_unused]] bool found_match = false; + + while (true) { + value_type arr[2]; + load_pair_array(&arr[0], current_slot); + + auto const first_slot_is_empty = + detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()); + auto const second_slot_is_empty = + detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()); + auto const first_equals = (not first_slot_is_empty and key_equal(arr[0].first, k)); + auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k)); + + if constexpr (is_outer) { + if (g.any(first_equals or second_equals)) { found_match = true; } + } + + count += (first_equals + second_equals); + + if (g.any(first_slot_is_empty or second_slot_is_empty)) { + if constexpr (is_outer) { + if ((not found_match) && (g.thread_rank() == 0)) { count++; } + } + return count; + } + + current_slot = next_slot(current_slot); + } + } + + /** + * @brief Counts the occurrence of a given key contained in multimap using scalar loads. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam CG Cooperative Group type + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the count operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return Number of matches found by the current thread + */ + template + __device__ __forceinline__ std::enable_if_t count( + CG const& g, Key const& k, KeyEqual key_equal) noexcept + { + std::size_t count = 0; + auto current_slot = initial_slot(g, k); + + [[maybe_unused]] bool found_match = false; + + while (true) { + value_type slot_contents = *reinterpret_cast(current_slot); + auto const& current_key = slot_contents.first; + + auto const slot_is_empty = + detail::bitwise_compare(current_key, this->get_empty_key_sentinel()); + auto const equals = not slot_is_empty and key_equal(current_key, k); + + if constexpr (is_outer) { + if (g.any(equals)) { found_match = true; } + } + + count += equals; + + if (g.any(slot_is_empty)) { + if constexpr (is_outer) { + if ((not found_match) && (g.thread_rank() == 0)) { count++; } + } + return count; + } + + current_slot = next_slot(current_slot); + } + } + + /** + * @brief Counts the occurrence of a given key/value pair contained in multimap using vector + * loads. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam CG Cooperative Group type + * @tparam PairEqual Binary callable type + * @param g The Cooperative Group used to perform the pair_count operation + * @param pair The pair to search for + * @param pair_equal The binary callable used to compare two pairs + * for equality + * @return Number of matches found by the current thread + */ + template + __device__ __forceinline__ std::enable_if_t pair_count( + CG const& g, value_type const& pair, PairEqual pair_equal) noexcept + { + std::size_t count = 0; + auto key = pair.first; + auto current_slot = initial_slot(g, key); + + [[maybe_unused]] bool found_match = false; + + while (true) { + value_type arr[2]; + load_pair_array(&arr[0], current_slot); + + auto const first_slot_is_empty = + detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()); + auto const second_slot_is_empty = + detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()); + + auto const first_slot_equals = (not first_slot_is_empty and pair_equal(arr[0], pair)); + auto const second_slot_equals = (not second_slot_is_empty and pair_equal(arr[1], pair)); + + if constexpr (is_outer) { + if (g.any(first_slot_equals or second_slot_equals)) { found_match = true; } + } + + count += (first_slot_equals + second_slot_equals); + + if (g.any(first_slot_is_empty or second_slot_is_empty)) { + if constexpr (is_outer) { + if ((not found_match) && (g.thread_rank() == 0)) { count++; } + } + return count; + } + + current_slot = next_slot(current_slot); + } + } + + /** + * @brief Counts the occurrence of a given key/value pair contained in multimap using scalar + * loads. + * + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam CG Cooperative Group type + * @tparam PairEqual Binary callable type + * @param g The Cooperative Group used to perform the pair_count operation + * @param pair The pair to search for + * @param pair_equal The binary callable used to compare two pairs + * for equality + * @return Number of matches found by the current thread + */ + template + __device__ __forceinline__ std::enable_if_t pair_count( + CG const& g, value_type const& pair, PairEqual pair_equal) noexcept + { + std::size_t count = 0; + auto key = pair.first; + auto current_slot = initial_slot(g, key); + + [[maybe_unused]] bool found_match = false; + + while (true) { + auto slot_contents = *reinterpret_cast(current_slot); + + auto const slot_is_empty = + detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel()); + + auto const equals = not slot_is_empty and pair_equal(slot_contents, pair); + + if constexpr (is_outer) { + if (g.any(equals)) { found_match = true; } + } + + count += equals; + + if (g.any(slot_is_empty)) { + if constexpr (is_outer) { + if ((not found_match) && (g.thread_rank() == 0)) { count++; } + } + return count; + } + + current_slot = next_slot(current_slot); + } + } + + /** + * @brief Retrieves all the matches of a given key contained in multimap using vector + * loads with per-flushing-CG shared memory buffer. + * + * For key `k` existing in the map, copies `k` and all associated values to unspecified + * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and + * `empty_value_sentinel()` into the output only if `is_outer` is true. + * + * @tparam buffer_size Size of the output buffer + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam FlushingCG Type of Cooperative Group used to flush output buffer + * @tparam ProbingCG Type of Cooperative Group used to retrieve + * @tparam atomicT Type of atomic storage + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam KeyEqual Binary callable type + * @param flushing_cg The Cooperative Group used to flush output buffer + * @param probing_cg The Cooperative Group used to retrieve + * @param k The key to search for + * @param flushing_cg_counter Pointer to the flushing cg counter + * @param output_buffer Shared memory buffer of the key/value pair sequence + * @param num_matches Size of the output sequence + * @param output_begin Beginning of the output sequence of key/value pairs + * @param key_equal The binary callable used to compare two keys + * for equality + */ + template + __device__ __forceinline__ void retrieve(FlushingCG const& flushing_cg, + ProbingCG const& probing_cg, + Key const& k, + uint32_t* flushing_cg_counter, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin, + KeyEqual key_equal) noexcept + { + const uint32_t cg_lane_id = probing_cg.thread_rank(); + + auto current_slot = initial_slot(probing_cg, k); + + bool running = true; + [[maybe_unused]] bool found_match = false; + + while (flushing_cg.any(running)) { + if (running) { + value_type arr[2]; + load_pair_array(&arr[0], current_slot); + + auto const first_slot_is_empty = + detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()); + auto const second_slot_is_empty = + detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()); + auto const first_equals = (not first_slot_is_empty and key_equal(arr[0].first, k)); + auto const second_equals = (not second_slot_is_empty and key_equal(arr[1].first, k)); + auto const first_exists = probing_cg.ballot(first_equals); + auto const second_exists = probing_cg.ballot(second_equals); + + if (first_exists or second_exists) { + if constexpr (is_outer) { found_match = true; } + + auto num_first_matches = __popc(first_exists); + auto num_second_matches = __popc(second_exists); + + uint32_t output_idx; + if (0 == cg_lane_id) { + output_idx = atomicAdd(flushing_cg_counter, (num_first_matches + num_second_matches)); + } + output_idx = probing_cg.shfl(output_idx, 0); + + if (first_equals) { + auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1)); + Key key = k; + output_buffer[output_idx + lane_offset] = + cuco::make_pair(std::move(key), std::move(arr[0].second)); + } + if (second_equals) { + auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1)); + Key key = k; + output_buffer[output_idx + num_first_matches + lane_offset] = + cuco::make_pair(std::move(key), std::move(arr[1].second)); + } + } + if (probing_cg.any(first_slot_is_empty or second_slot_is_empty)) { + running = false; + if constexpr (is_outer) { + if ((not found_match) && (cg_lane_id == 0)) { + auto output_idx = atomicAdd(flushing_cg_counter, 1); + Key key = k; + output_buffer[output_idx] = cuco::make_pair( + std::move(key), std::move(this->get_empty_value_sentinel())); + } + } + } + } // if running + + flushing_cg.sync(); + if (*flushing_cg_counter + flushing_cg.size() * vector_width() > buffer_size) { + flush_output_buffer( + flushing_cg, *flushing_cg_counter, output_buffer, num_matches, output_begin); + // First lane reset warp-level counter + if (flushing_cg.thread_rank() == 0) { *flushing_cg_counter = 0; } + } + + current_slot = next_slot(current_slot); + } // while running + } + + /** + * @brief Retrieves all the matches of a given key contained in multimap using scalar + * loads with per-CG shared memory buffer. + * + * For key `k` existing in the map, copies `k` and all associated values to unspecified + * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and + * `empty_value_sentinel()` into the output only if `is_outer` is true. + * + * @tparam buffer_size Size of the output buffer + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam CG Cooperative Group type + * @tparam atomicT Type of atomic storage + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to retrieve + * @param k The key to search for + * @param cg_counter Pointer to the CG counter + * @param output_buffer Shared memory buffer of the key/value pair sequence + * @param num_matches Size of the output sequence + * @param output_begin Beginning of the output sequence of key/value pairs + * @param key_equal The binary callable used to compare two keys + * for equality + */ + template + __device__ __forceinline__ void retrieve(CG const& g, + Key const& k, + uint32_t* cg_counter, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin, + KeyEqual key_equal) noexcept + { + const uint32_t lane_id = g.thread_rank(); + + auto current_slot = initial_slot(g, k); + + bool running = true; + [[maybe_unused]] bool found_match = false; + + while (running) { + // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation + // is unsafe! + static_assert(sizeof(Key) == sizeof(cuda::atomic)); + static_assert(sizeof(Value) == sizeof(cuda::atomic)); + value_type slot_contents = *reinterpret_cast(current_slot); + + auto const slot_is_empty = + detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel()); + auto const equals = (not slot_is_empty and key_equal(slot_contents.first, k)); + uint32_t output_idx = *cg_counter; + auto const exists = g.ballot(equals); + + if (exists) { + if constexpr (is_outer) { found_match = true; } + auto num_matches = __popc(exists); + if (equals) { + // Each match computes its lane-level offset + auto lane_offset = __popc(exists & ((1 << lane_id) - 1)); + Key key = k; + output_buffer[output_idx + lane_offset] = + cuco::make_pair(std::move(key), std::move(slot_contents.second)); + } + if (0 == lane_id) { (*cg_counter) += num_matches; } + } + if (g.any(slot_is_empty)) { + running = false; + if constexpr (is_outer) { + if ((not found_match) && (lane_id == 0)) { + output_idx = (*cg_counter)++; + Key key = k; + output_buffer[output_idx] = cuco::make_pair( + std::move(key), std::move(this->get_empty_value_sentinel())); + } + } + } + + g.sync(); + + // Flush if the next iteration won't fit into buffer + if ((*cg_counter + g.size()) > buffer_size) { + flush_output_buffer(g, *cg_counter, output_buffer, num_matches, output_begin); + // First lane reset CG-level counter + if (lane_id == 0) { *cg_counter = 0; } + } + current_slot = next_slot(current_slot); + } // while running + } + + /** + * @brief Retrieves all the matches of a given pair contained in multimap using vector + * loads with per-flushing-CG shared memory buffer. + * + * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations + * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in + * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies + * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if + * `is_outer` is true. + * + * @tparam buffer_size Size of the output buffer + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam FlushingCG Type of Cooperative Group used to flush output buffer + * @tparam ProbingCG Type of Cooperative Group used to retrieve + * @tparam atomicT Type of atomic storage + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam PairEqual Binary callable type + * @param flushing_cg The Cooperative Group used to flush output buffer + * @param probing_cg The Cooperative Group used to retrieve + * @param pair The pair to search for + * @param flushing_cg_counter Pointer to the flushing CG counter + * @param probe_output_buffer Buffer of the matched probe pair sequence + * @param contained_output_buffer Buffer of the matched contained pair sequence + * @param num_matches Size of the output sequence + * @param probe_output_begin Beginning of the output sequence of the matched probe pairs + * @param contained_output_begin Beginning of the output sequence of the matched contained + * pairs + * @param pair_equal The binary callable used to compare two pairs for equality + */ + template + __device__ __forceinline__ void pair_retrieve(FlushingCG const& flushing_cg, + ProbingCG const& probing_cg, + value_type const& pair, + uint32_t* flushing_cg_counter, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal) noexcept + { + const uint32_t cg_lane_id = probing_cg.thread_rank(); + + auto key = pair.first; + auto current_slot = initial_slot(probing_cg, key); + + bool running = true; + [[maybe_unused]] bool found_match = false; + + while (flushing_cg.any(running)) { + if (running) { + value_type arr[2]; + load_pair_array(&arr[0], current_slot); + + auto const first_slot_is_empty = + detail::bitwise_compare(arr[0].first, this->get_empty_key_sentinel()); + auto const second_slot_is_empty = + detail::bitwise_compare(arr[1].first, this->get_empty_key_sentinel()); + auto const first_equals = (not first_slot_is_empty and pair_equal(arr[0], pair)); + auto const second_equals = (not second_slot_is_empty and pair_equal(arr[1], pair)); + auto const first_exists = probing_cg.ballot(first_equals); + auto const second_exists = probing_cg.ballot(second_equals); + + if (first_exists or second_exists) { + if constexpr (is_outer) { found_match = true; } + + auto num_first_matches = __popc(first_exists); + auto num_second_matches = __popc(second_exists); + + uint32_t output_idx; + if (0 == cg_lane_id) { + output_idx = atomicAdd(flushing_cg_counter, (num_first_matches + num_second_matches)); + } + output_idx = probing_cg.shfl(output_idx, 0); + + if (first_equals) { + auto lane_offset = __popc(first_exists & ((1 << cg_lane_id) - 1)); + probe_output_buffer[output_idx + lane_offset] = pair; + contained_output_buffer[output_idx + lane_offset] = arr[0]; + } + if (second_equals) { + auto lane_offset = __popc(second_exists & ((1 << cg_lane_id) - 1)); + probe_output_buffer[output_idx + num_first_matches + lane_offset] = pair; + contained_output_buffer[output_idx + num_first_matches + lane_offset] = arr[1]; + } + } + if (probing_cg.any(first_slot_is_empty or second_slot_is_empty)) { + running = false; + if constexpr (is_outer) { + if ((not found_match) && (cg_lane_id == 0)) { + auto output_idx = atomicAdd(flushing_cg_counter, 1); + probe_output_buffer[output_idx] = pair; + contained_output_buffer[output_idx] = + cuco::make_pair(std::move(this->get_empty_key_sentinel()), + std::move(this->get_empty_value_sentinel())); + } + } + } + } // if running + + flushing_cg.sync(); + if (*flushing_cg_counter + flushing_cg.size() * vector_width() > buffer_size) { + flush_output_buffer(flushing_cg, + *flushing_cg_counter, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin); + // First lane reset warp-level counter + if (flushing_cg.thread_rank() == 0) { *flushing_cg_counter = 0; } + } + + current_slot = next_slot(current_slot); + } // while running + } + + /** + * @brief Retrieves all the matches of a given pair contained in multimap using scalar + * loads with per-CG shared memory buffer. + * + * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations + * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in + * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies + * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output only if + * `is_outer` is true. + * + * @tparam buffer_size Size of the output buffer + * @tparam is_outer Boolean flag indicating whether outer join is peformed + * @tparam CG Cooperative Group type + * @tparam atomicT Type of atomic storage + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam PairEqual Binary callable type + * @param g The Cooperative Group used to retrieve + * @param pair The pair to search for + * @param cg_counter Pointer to the CG counter + * @param probe_output_buffer Buffer of the matched probe pair sequence + * @param contained_output_buffer Buffer of the matched contained pair sequence + * @param num_matches Size of the output sequence + * @param probe_output_begin Beginning of the output sequence of the matched probe pairs + * @param contained_output_begin Beginning of the output sequence of the matched contained + * pairs + * @param pair_equal The binary callable used to compare two pairs for equality + */ + template + __device__ __forceinline__ void pair_retrieve(CG const& g, + value_type const& pair, + uint32_t* cg_counter, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal) noexcept + { + const uint32_t lane_id = g.thread_rank(); + + auto key = pair.first; + auto current_slot = initial_slot(g, key); + + bool running = true; + [[maybe_unused]] bool found_match = false; + + while (running) { + // TODO: Replace reinterpret_cast with atomic ref when possible. The current implementation + // is unsafe! + static_assert(sizeof(Key) == sizeof(cuda::atomic)); + static_assert(sizeof(Value) == sizeof(cuda::atomic)); + value_type slot_contents = *reinterpret_cast(current_slot); + + auto const slot_is_empty = + detail::bitwise_compare(slot_contents.first, this->get_empty_key_sentinel()); + auto const equals = (not slot_is_empty and pair_equal(slot_contents, pair)); + uint32_t output_idx = *cg_counter; + auto const exists = g.ballot(equals); + + if (exists) { + if constexpr (is_outer) { found_match = true; } + auto num_matches = __popc(exists); + if (equals) { + // Each match computes its lane-level offset + auto lane_offset = __popc(exists & ((1 << lane_id) - 1)); + probe_output_buffer[output_idx + lane_offset] = pair; + contained_output_buffer[output_idx + lane_offset] = slot_contents; + } + if (0 == lane_id) { (*cg_counter) += num_matches; } + } + if (g.any(slot_is_empty)) { + running = false; + if constexpr (is_outer) { + if ((not found_match) && (lane_id == 0)) { + output_idx = (*cg_counter)++; + probe_output_buffer[output_idx] = pair; + contained_output_buffer[output_idx] = + cuco::make_pair(std::move(this->get_empty_key_sentinel()), + std::move(this->get_empty_value_sentinel())); + } + } + } + + g.sync(); + + // Flush if the next iteration won't fit into buffer + if ((*cg_counter + g.size()) > buffer_size) { + flush_output_buffer(g, + *cg_counter, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin); + // First lane reset CG-level counter + if (lane_id == 0) { *cg_counter = 0; } + } + current_slot = next_slot(current_slot); + } // while running + } +}; // class device_view_impl + +} // namespace cuco diff --git a/include/cuco/detail/static_multimap/kernels.cuh b/include/cuco/detail/static_multimap/kernels.cuh new file mode 100644 index 000000000..83aa2c640 --- /dev/null +++ b/include/cuco/detail/static_multimap/kernels.cuh @@ -0,0 +1,533 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include + +namespace cuco { +namespace detail { +namespace cg = cooperative_groups; + +/** + * @brief Initializes each slot in the flat `slots` storage to contain `k` and `v`. + * + * Each space in `slots` that can hold a key value pair is initialized to a + * `pair_atomic_type` containing the key `k` and the value `v`. + * + * @tparam atomic_key_type Type of the `Key` atomic container + * @tparam atomic_mapped_type Type of the `Value` atomic container + * @tparam Key key type + * @tparam Value value type + * @tparam pair_atomic_type key/value pair type + * @param slots Pointer to flat storage for the map's key/value pairs + * @param k Key to which all keys in `slots` are initialized + * @param v Value to which all values in `slots` are initialized + * @param size Size of the storage pointed to by `slots` + */ +template +__global__ void initialize(pair_atomic_type* const slots, Key k, Value v, std::size_t size) +{ + auto tid = threadIdx.x + blockIdx.x * blockDim.x; + while (tid < size) { + new (&slots[tid].first) atomic_key_type{k}; + new (&slots[tid].second) atomic_mapped_type{v}; + tid += gridDim.x * blockDim.x; + } +} + +/** + * @brief Inserts all key/value pairs in the range `[first, last)`. + * + * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform each + * key/value insertion. This provides a significant boost in throughput compared to the non + * Cooperative Group `insert` at moderate to high load factors. + * + * @tparam block_size The size of the thread block + * @tparam tile_size The number of threads in the Cooperative Groups used to perform + * inserts + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam viewT Type of device view allowing access of hash map storage + * + * @param first Beginning of the sequence of key/value pairs + * @param last End of the sequence of key/value pairs + * @param view Mutable device view used to access the hash map's slot storage + */ +template +__global__ void insert(InputIt first, InputIt last, viewT view) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; + auto it = first + tid / tile_size; + + while (it < last) { + // force conversion to value_type + typename viewT::value_type const insert_pair{*it}; + view.insert(tile, insert_pair); + it += (gridDim.x * block_size) / tile_size; + } +} + +/** + * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred` of the + * corresponding stencil returns true. + * + * The key/value pair `*(first + i)` is inserted if `pred( *(stencil + i) )` returns true. + * + * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform each + * key/value insertion. This provides a significant boost in throughput compared to the non + * Cooperative Group `insert` at moderate to high load factors. + * + * @tparam block_size The size of the thread block + * @tparam tile_size The number of threads in the Cooperative Groups used to perform + * inserts + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam StencilIt Device accessible random access iterator whose value_type is + * convertible to Predicate's argument type + * @tparam viewT Type of device view allowing access of hash map storage + * @tparam Predicate Unary predicate callable whose return type must be convertible to `bool` and + * argument type is convertible from `std::iterator_traits::value_type`. + * @param first Beginning of the sequence of key/value pairs + * @param s Beginning of the stencil sequence + * @param n Number of elements to insert + * @param view Mutable device view used to access the hash map's slot storage + * @param pred Predicate to test on every element in the range `[s, s + n)` + */ +template +__global__ void insert_if_n(InputIt first, StencilIt s, std::size_t n, viewT view, Predicate pred) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto const tid = block_size * blockIdx.x + threadIdx.x; + auto i = tid / tile_size; + + while (i < n) { + if (pred(*(s + i))) { + typename viewT::value_type const insert_pair{*(first + i)}; + // force conversion to value_type + view.insert(tile, insert_pair); + } + i += (gridDim.x * block_size) / tile_size; + } +} + +/** + * @brief Indicates whether the keys in the range `[first, last)` are contained in the map. + * + * Stores `true` or `false` to `(output + i)` indicating if the key `*(first + i)` exists in the + * map. + * + * Uses the CUDA Cooperative Groups API to leverage groups of multiple threads to perform the + * contains operation for each key. This provides a significant boost in throughput compared + * to the non Cooperative Group `contains` at moderate to high load factors. + * + * @tparam block_size The size of the thread block + * @tparam tile_size The number of threads in the Cooperative Groups + * @tparam InputIt Device accessible input iterator whose `value_type` is + * convertible to the map's `key_type` + * @tparam OutputIt Device accessible output iterator whose `value_type` is convertible from `bool` + * @tparam viewT Type of device view allowing access of hash map storage + * @tparam KeyEqual Binary callable type + * @param first Beginning of the sequence of keys + * @param last End of the sequence of keys + * @param output_begin Beginning of the sequence of booleans for the presence of each key + * @param view Device view used to access the hash map's slot storage + * @param key_equal The binary function to compare two keys for equality + */ +template +__global__ void contains( + InputIt first, InputIt last, OutputIt output_begin, viewT view, KeyEqual key_equal) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; + auto key_idx = tid / tile_size; + __shared__ bool writeBuffer[block_size]; + + while (first + key_idx < last) { + auto key = *(first + key_idx); + auto found = view.contains(tile, key, key_equal); + + /* + * The ld.relaxed.gpu instruction used in view.find causes L1 to + * flush more frequently, causing increased sector stores from L2 to global memory. + * By writing results to shared memory and then synchronizing before writing back + * to global, we no longer rely on L1, preventing the increase in sector stores from + * L2 to global and improving performance. + */ + if (tile.thread_rank() == 0) { writeBuffer[threadIdx.x / tile_size] = found; } + __syncthreads(); + if (tile.thread_rank() == 0) { + *(output_begin + key_idx) = writeBuffer[threadIdx.x / tile_size]; + } + key_idx += (gridDim.x * block_size) / tile_size; + } +} + +/** + * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. + * + * For each key, `k = *(first + i)`, counts all matching keys, `k'`, as determined by `key_equal(k, + * k')` and stores the sum of all matches for all keys to `num_matches`. If `k` does not have any + * matches, it contributes 1 to the final sum only if `is_outer` is true. + * + * @tparam block_size The size of the thread block + * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts + * @tparam uses_vector_load Boolean flag indicating whether vector loads are used or not + * @tparam is_outer Boolean flag indicating whether non-matches are counted + * @tparam InputIt Device accessible input iterator whose `value_type` is convertible to the map's + * `key_type` + * @tparam atomicT Type of atomic storage + * @tparam viewT Type of device view allowing access of hash map storage + * @tparam KeyEqual Binary callable + * @param first Beginning of the sequence of keys to count + * @param last End of the sequence of keys to count + * @param num_matches The number of all the matches for a sequence of keys + * @param view Device view used to access the hash map's slot storage + * @param key_equal Binary function to compare two keys for equality + */ +template +__global__ void count( + InputIt first, InputIt last, atomicT* num_matches, viewT view, KeyEqual key_equal) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; + auto key_idx = tid / tile_size; + + typedef cub::BlockReduce BlockReduce; + __shared__ typename BlockReduce::TempStorage temp_storage; + std::size_t thread_num_matches = 0; + + while (first + key_idx < last) { + auto key = *(first + key_idx); + if constexpr (is_outer) { + thread_num_matches += view.count_outer(tile, key, key_equal); + } else { + thread_num_matches += view.count(tile, key, key_equal); + } + key_idx += (gridDim.x * block_size) / tile_size; + } + + // compute number of successfully inserted elements for each block + // and atomically add to the grand total + std::size_t block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches); + if (threadIdx.x == 0) { + num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed); + } +} + +/** + * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap. + * + * For pair, `p = *(first + i)`, counts all matching pairs, `p'`, as determined by `pair_equal(p, + * p')` and stores the sum of all matches for all pairs to `num_matches`. If `p` does not have any + * matches, it contributes 1 to the final sum only if `is_outer` is true. + * + * @tparam block_size The size of the thread block + * @tparam tile_size The number of threads in the Cooperative Groups used to perform counts + * @tparam is_outer Boolean flag indicating whether non-matches are counted + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam atomicT Type of atomic storage + * @tparam viewT Type of device view allowing access of hash map storage + * @tparam PairEqual Binary callable + * @param first Beginning of the sequence of pairs to count + * @param last End of the sequence of pairs to count + * @param num_matches The number of all the matches for a sequence of pairs + * @param view Device view used to access the hash map's slot storage + * @param pair_equal Binary function to compare two pairs for equality + */ +template +__global__ void pair_count( + InputIt first, InputIt last, atomicT* num_matches, viewT view, PairEqual pair_equal) +{ + auto tile = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; + auto pair_idx = tid / tile_size; + + typedef cub::BlockReduce BlockReduce; + __shared__ typename BlockReduce::TempStorage temp_storage; + std::size_t thread_num_matches = 0; + + while (first + pair_idx < last) { + typename viewT::value_type const pair = *(first + pair_idx); + if constexpr (is_outer) { + thread_num_matches += view.pair_count_outer(tile, pair, pair_equal); + } else { + thread_num_matches += view.pair_count(tile, pair, pair_equal); + } + pair_idx += (gridDim.x * block_size) / tile_size; + } + + // compute number of successfully inserted elements for each block + // and atomically add to the grand total + std::size_t block_num_matches = BlockReduce(temp_storage).Sum(thread_num_matches); + if (threadIdx.x == 0) { + num_matches->fetch_add(block_num_matches, cuda::std::memory_order_relaxed); + } +} + +/** + * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`. + * + * For key `k = *(first + i)` existing in the map, copies `k` and all associated values to + * unspecified locations in `[output_begin, output_end)`. If `k` does not have any matches, copies + * `k` and `empty_value_sentinel()` into the output only if `is_outer` is true. + * + * Behavior is undefined if the total number of matching keys exceeds `std::distance(output_begin, + * output_begin + *num_matches - 1)`. Use `count()` to determine the size of the output range. + * + * @tparam block_size The size of the thread block + * @tparam flushing_cg_size The size of the CG used to flush output buffers + * @tparam probing_cg_size The size of the CG for parallel retrievals + * @tparam buffer_size Size of the output buffer + * @tparam is_outer Boolean flag indicating whether non-matches are included in the output + * @tparam InputIt Device accessible input iterator whose `value_type` is + * convertible to the map's `key_type` + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam atomicT Type of atomic storage + * @tparam viewT Type of device view allowing access of hash map storage + * @tparam KeyEqual Binary callable type + * @param first Beginning of the sequence of keys + * @param last End of the sequence of keys + * @param output_begin Beginning of the sequence of values retrieved for each key + * @param num_matches Size of the output sequence + * @param view Device view used to access the hash map's slot storage + * @param key_equal The binary function to compare two keys for equality + */ +template +__global__ void retrieve(InputIt first, + InputIt last, + OutputIt output_begin, + atomicT* num_matches, + viewT view, + KeyEqual key_equal) +{ + using pair_type = typename viewT::value_type; + + constexpr uint32_t num_flushing_cgs = block_size / flushing_cg_size; + const uint32_t flushing_cg_id = threadIdx.x / flushing_cg_size; + + auto flushing_cg = cg::tiled_partition(cg::this_thread_block()); + auto probing_cg = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; + auto key_idx = tid / probing_cg_size; + + __shared__ pair_type output_buffer[num_flushing_cgs][buffer_size]; + // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization + // warning issue is solved __shared__ atomicT counter[num_flushing_cgs][buffer_size]; + __shared__ uint32_t flushing_cg_counter[num_flushing_cgs]; + + if (flushing_cg.thread_rank() == 0) { flushing_cg_counter[flushing_cg_id] = 0; } + + while (flushing_cg.any(first + key_idx < last)) { + bool active_flag = first + key_idx < last; + auto active_flushing_cg = cg::binary_partition(flushing_cg, active_flag); + + if (active_flag) { + auto key = *(first + key_idx); + if constexpr (is_outer) { + view.retrieve_outer(active_flushing_cg, + probing_cg, + key, + &flushing_cg_counter[flushing_cg_id], + output_buffer[flushing_cg_id], + num_matches, + output_begin, + key_equal); + } else { + view.retrieve(active_flushing_cg, + probing_cg, + key, + &flushing_cg_counter[flushing_cg_id], + output_buffer[flushing_cg_id], + num_matches, + output_begin, + key_equal); + } + } + key_idx += (gridDim.x * block_size) / probing_cg_size; + } + + // Final flush of output buffer + if (flushing_cg_counter[flushing_cg_id] > 0) { + view.flush_output_buffer(flushing_cg, + flushing_cg_counter[flushing_cg_id], + output_buffer[flushing_cg_id], + num_matches, + output_begin); + } +} + +/** + * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`. + * + * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to unspecified + * locations in `probe_output_begin`, and slot[j] is stored to unspecified locations in + * `contained_output_begin`. If the given pair has no matches in the map, copies *(first + i) in + * `probe_output_begin` and a pair of `empty_key_sentinel` and `empty_value_sentinel` in + * `contained_output_begin` only when `is_outer` is `true`. + * + * Behavior is undefined if the total number of matching pairs exceeds `std::distance(output_begin, + * output_begin + *num_matches - 1)`. Use `pair_count()` to determine the size of the output range. + * + * @tparam block_size The size of the thread block + * @tparam flushing_cg_size The size of the CG used to flush output buffers + * @tparam probing_cg_size The size of the CG for parallel retrievals + * @tparam buffer_size Size of the output buffer + * @tparam is_outer Boolean flag indicating whether non-matches are included in the output + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam atomicT Type of atomic storage + * @tparam viewT Type of device view allowing access of hash map storage + * @tparam PairEqual Binary callable type + * @param first Beginning of the sequence of keys + * @param last End of the sequence of keys + * @param probe_output_begin Beginning of the sequence of the matched probe pairs + * @param contained_output_begin Beginning of the sequence of the matched contained pairs + * @param num_matches Size of the output sequence + * @param view Device view used to access the hash map's slot storage + * @param pair_equal The binary function to compare two pairs for equality + */ +template +__global__ void pair_retrieve(InputIt first, + InputIt last, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + atomicT* num_matches, + viewT view, + PairEqual pair_equal) +{ + using pair_type = typename viewT::value_type; + + constexpr uint32_t num_flushing_cgs = block_size / flushing_cg_size; + const uint32_t flushing_cg_id = threadIdx.x / flushing_cg_size; + + auto flushing_cg = cg::tiled_partition(cg::this_thread_block()); + auto probing_cg = cg::tiled_partition(cg::this_thread_block()); + auto tid = block_size * blockIdx.x + threadIdx.x; + auto pair_idx = tid / probing_cg_size; + + __shared__ pair_type probe_output_buffer[num_flushing_cgs][buffer_size]; + __shared__ pair_type contained_output_buffer[num_flushing_cgs][buffer_size]; + // TODO: replace this with shared memory cuda::atomic variables once the dynamiic initialization + // warning issue is solved __shared__ atomicT counter[num_flushing_cgs][buffer_size]; + __shared__ uint32_t flushing_cg_counter[num_flushing_cgs]; + + if (flushing_cg.thread_rank() == 0) { flushing_cg_counter[flushing_cg_id] = 0; } + + while (flushing_cg.any(first + pair_idx < last)) { + bool active_flag = first + pair_idx < last; + auto active_flushing_cg = cg::binary_partition(flushing_cg, active_flag); + + if (active_flag) { + pair_type pair = *(first + pair_idx); + if constexpr (is_outer) { + view.pair_retrieve_outer(active_flushing_cg, + probing_cg, + pair, + &flushing_cg_counter[flushing_cg_id], + probe_output_buffer[flushing_cg_id], + contained_output_buffer[flushing_cg_id], + num_matches, + probe_output_begin, + contained_output_begin, + pair_equal); + } else { + view.pair_retrieve(active_flushing_cg, + probing_cg, + pair, + &flushing_cg_counter[flushing_cg_id], + probe_output_buffer[flushing_cg_id], + contained_output_buffer[flushing_cg_id], + num_matches, + probe_output_begin, + contained_output_begin, + pair_equal); + } + } + pair_idx += (gridDim.x * block_size) / probing_cg_size; + } + + // Final flush of output buffer + if (flushing_cg_counter[flushing_cg_id] > 0) { + view.flush_output_buffer(flushing_cg, + flushing_cg_counter[flushing_cg_id], + probe_output_buffer[flushing_cg_id], + contained_output_buffer[flushing_cg_id], + num_matches, + probe_output_begin, + contained_output_begin); + } +} + +} // namespace detail +} // namespace cuco diff --git a/include/cuco/detail/static_multimap/static_multimap.inl b/include/cuco/detail/static_multimap/static_multimap.inl new file mode 100644 index 000000000..6d7c36f00 --- /dev/null +++ b/include/cuco/detail/static_multimap/static_multimap.inl @@ -0,0 +1,817 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include + +#include + +namespace { +/** + * @brief Device functor used to determine if a slot is filled. + */ +template +struct slot_is_filled { + slot_is_filled(Key s) : empty_key_sentinel{s} {} + __device__ __forceinline__ bool operator()(Key const& k) { return k != empty_key_sentinel; } + Key empty_key_sentinel; +}; +} // anonymous namespace + +namespace cuco { + +template +static_multimap::static_multimap( + std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel, + cudaStream_t stream, + Allocator const& alloc) + : capacity_{cuco::detail::get_valid_capacity( + capacity)}, + empty_key_sentinel_{empty_key_sentinel}, + empty_value_sentinel_{empty_value_sentinel}, + counter_allocator_{alloc}, + slot_allocator_{alloc}, + stream_{stream}, + delete_counter_{counter_allocator_, stream_}, + delete_slots_{slot_allocator_, capacity_, stream_}, + d_counter_{counter_allocator_.allocate(1, stream_), delete_counter_}, + slots_{slot_allocator_.allocate(capacity_, stream_), delete_slots_} +{ + auto constexpr block_size = 128; + auto constexpr stride = 4; + auto const grid_size = (get_capacity() + stride * block_size - 1) / (stride * block_size); + + detail::initialize<<>>( + slots_.get(), empty_key_sentinel, empty_value_sentinel, get_capacity()); +} + +template +template +void static_multimap::insert(InputIt first, + InputIt last, + cudaStream_t stream) +{ + auto num_keys = std::distance(first, last); + auto view = get_device_mutable_view(); + + auto constexpr block_size = 128; + auto constexpr stride = 1; + auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size); + + detail::insert + <<>>(first, first + num_keys, view); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); +} + +template +template +void static_multimap::insert_if( + InputIt first, InputIt last, StencilIt stencil, Predicate pred, cudaStream_t stream) +{ + auto num_elements = std::distance(first, last); + auto view = get_device_mutable_view(); + + auto constexpr block_size = 128; + auto constexpr stride = 1; + auto const grid_size = + (cg_size() * num_elements + stride * block_size - 1) / (stride * block_size); + + detail::insert_if_n + <<>>(first, stencil, num_elements, view, pred); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); +} + +template +template +void static_multimap::contains( + InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + auto constexpr block_size = 128; + auto constexpr stride = 1; + auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size); + + detail::contains + <<>>(first, last, output_begin, view, key_equal); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); +} + +template +template +std::size_t static_multimap::count( + InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + auto constexpr is_outer = false; + auto constexpr block_size = 128; + auto constexpr stride = 1; + + auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::count + <<>>(first, last, d_counter_.get(), view, key_equal); + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + return h_counter; +} + +template +template +std::size_t static_multimap::count_outer( + InputIt first, InputIt last, cudaStream_t stream, KeyEqual key_equal) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + auto constexpr is_outer = true; + auto constexpr block_size = 128; + auto constexpr stride = 1; + + auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::count + <<>>(first, last, d_counter_.get(), view, key_equal); + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + return h_counter; +} + +template +template +std::size_t static_multimap::pair_count( + InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + bool constexpr is_outer = false; + + auto constexpr block_size = 128; + auto constexpr stride = 1; + auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::pair_count + <<>>(first, last, d_counter_.get(), view, pair_equal); + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + return h_counter; +} + +template +template +std::size_t static_multimap::pair_count_outer( + InputIt first, InputIt last, PairEqual pair_equal, cudaStream_t stream) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + bool constexpr is_outer = true; + + auto constexpr block_size = 128; + auto constexpr stride = 1; + auto const grid_size = (cg_size() * num_keys + stride * block_size - 1) / (stride * block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::pair_count + <<>>(first, last, d_counter_.get(), view, pair_equal); + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + return h_counter; +} + +template +template +OutputIt static_multimap::retrieve( + InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + // Using per-warp buffer for vector loads and per-CG buffer for scalar loads + constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u); + constexpr auto block_size = 128; + constexpr auto is_outer = false; + + auto const flushing_cg_size = [&]() { + if constexpr (uses_vector_load()) { return warp_size(); } + return cg_size(); + }(); + + auto const grid_size = detail::get_grid_size(detail::retrieve, + block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::retrieve + <<>>( + first, last, output_begin, d_counter_.get(), view, key_equal); + + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + auto output_end = output_begin + h_counter; + return output_end; +} + +template +template +OutputIt static_multimap::retrieve_outer( + InputIt first, InputIt last, OutputIt output_begin, cudaStream_t stream, KeyEqual key_equal) const +{ + auto num_keys = std::distance(first, last); + auto view = get_device_view(); + + // Using per-warp buffer for vector loads and per-CG buffer for scalar loads + constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u); + constexpr auto block_size = 128; + constexpr auto is_outer = true; + + auto const flushing_cg_size = [&]() { + if constexpr (uses_vector_load()) { return warp_size(); } + return cg_size(); + }(); + + auto const grid_size = detail::get_grid_size(detail::retrieve, + block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::retrieve + <<>>( + first, last, output_begin, d_counter_.get(), view, key_equal); + + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + auto output_end = output_begin + h_counter; + return output_end; +} + +template +template +std::pair +static_multimap::pair_retrieve( + InputIt first, + InputIt last, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal, + cudaStream_t stream) const +{ + auto num_pairs = std::distance(first, last); + auto view = get_device_view(); + + // Using per-warp buffer for vector loads and per-CG buffer for scalar loads + constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u); + constexpr auto block_size = 128; + constexpr auto is_outer = false; + constexpr auto stride = 1; + + auto const flushing_cg_size = [&]() { + if constexpr (uses_vector_load()) { return warp_size(); } + return cg_size(); + }(); + + auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::pair_retrieve + <<>>( + first, last, probe_output_begin, contained_output_begin, d_counter_.get(), view, pair_equal); + + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + return std::make_pair(probe_output_begin + h_counter, contained_output_begin + h_counter); +} + +template +template +std::pair +static_multimap::pair_retrieve_outer( + InputIt first, + InputIt last, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal, + cudaStream_t stream) const +{ + auto num_pairs = std::distance(first, last); + auto view = get_device_view(); + + // Using per-warp buffer for vector loads and per-CG buffer for scalar loads + constexpr auto buffer_size = uses_vector_load() ? (warp_size() * 3u) : (cg_size() * 3u); + constexpr auto block_size = 128; + constexpr auto is_outer = true; + constexpr auto stride = 1; + + auto const flushing_cg_size = [&]() { + if constexpr (uses_vector_load()) { return warp_size(); } + return cg_size(); + }(); + + auto const grid_size = (cg_size() * num_pairs + stride * block_size - 1) / (stride * block_size); + + cudaMemsetAsync(d_counter_.get(), 0, sizeof(atomic_ctr_type), stream); + std::size_t h_counter; + + detail::pair_retrieve + <<>>( + first, last, probe_output_begin, contained_output_begin, d_counter_.get(), view, pair_equal); + + CUCO_CUDA_TRY(cudaMemcpyAsync( + &h_counter, d_counter_.get(), sizeof(atomic_ctr_type), cudaMemcpyDeviceToHost, stream)); + CUCO_CUDA_TRY(cudaStreamSynchronize(stream)); + + return std::make_pair(probe_output_begin + h_counter, contained_output_begin + h_counter); +} + +template +__device__ __forceinline__ void +static_multimap::device_mutable_view::insert( + cooperative_groups::thread_block_tile const& g, + value_type const& insert_pair) noexcept +{ + impl_.insert(g, insert_pair); +} + +template +template +__device__ __forceinline__ static_multimap::device_view +static_multimap::device_view::make_copy( + CG g, pair_atomic_type* const memory_to_use, device_view source_device_view) noexcept +{ +#if defined(CUCO_HAS_CUDA_BARRIER) + __shared__ cuda::barrier barrier; + if (g.thread_rank() == 0) { init(&barrier, g.size()); } + g.sync(); + + cuda::memcpy_async(g, + memory_to_use, + source_device_view.get_slots(), + sizeof(pair_atomic_type) * source_device_view.get_capacity(), + barrier); + + barrier.arrive_and_wait(); +#else + pair_atomic_type const* const slots_ptr = source_device_view.get_slots(); + for (std::size_t i = g.thread_rank(); i < source_device_view.get_capacity(); i += g.size()) { + new (&memory_to_use[i].first) + atomic_key_type{slots_ptr[i].first.load(cuda::memory_order_relaxed)}; + new (&memory_to_use[i].second) + atomic_mapped_type{slots_ptr[i].second.load(cuda::memory_order_relaxed)}; + } + g.sync(); +#endif + + return device_view(memory_to_use, + source_device_view.get_capacity(), + source_device_view.get_empty_key_sentinel(), + source_device_view.get_empty_value_sentinel()); +} + +template +template +__device__ __forceinline__ void +static_multimap::device_view::flush_output_buffer( + CG const& g, + uint32_t const num_outputs, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin) noexcept +{ + impl_.flush_output_buffer(g, num_outputs, output_buffer, num_matches, output_begin); +} + +template +template +__device__ __forceinline__ void +static_multimap::device_view::flush_output_buffer( + CG const& g, + uint32_t const num_outputs, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin) noexcept +{ + impl_.flush_output_buffer(g, + num_outputs, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin); +} + +template +template +__device__ __forceinline__ bool +static_multimap::device_view::contains( + cooperative_groups::thread_block_tile const& g, + Key const& k, + KeyEqual key_equal) noexcept +{ + return impl_.contains(g, k, key_equal); +} + +template +template +__device__ __forceinline__ std::size_t +static_multimap::device_view::count( + cooperative_groups::thread_block_tile const& g, + Key const& k, + KeyEqual key_equal) noexcept +{ + constexpr bool is_outer = false; + return impl_.count(g, k, key_equal); +} + +template +template +__device__ __forceinline__ std::size_t +static_multimap::device_view::count_outer( + cooperative_groups::thread_block_tile const& g, + Key const& k, + KeyEqual key_equal) noexcept +{ + constexpr bool is_outer = true; + return impl_.count(g, k, key_equal); +} + +template +template +__device__ __forceinline__ std::size_t +static_multimap::device_view::pair_count( + cooperative_groups::thread_block_tile const& g, + value_type const& pair, + PairEqual pair_equal) noexcept +{ + constexpr bool is_outer = false; + return impl_.pair_count(g, pair, pair_equal); +} + +template +template +__device__ __forceinline__ std::size_t +static_multimap::device_view::pair_count_outer( + cooperative_groups::thread_block_tile const& g, + value_type const& pair, + PairEqual pair_equal) noexcept +{ + constexpr bool is_outer = true; + return impl_.pair_count(g, pair, pair_equal); +} + +template +template +__device__ __forceinline__ void +static_multimap::device_view::retrieve( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + Key const& k, + uint32_t* flushing_cg_counter, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin, + KeyEqual key_equal) noexcept +{ + constexpr bool is_outer = false; + if constexpr (uses_vector_load()) { + impl_.retrieve(flushing_cg, + probing_cg, + k, + flushing_cg_counter, + output_buffer, + num_matches, + output_begin, + key_equal); + } else // In the case of scalar load, flushing CG is the same as probing CG + { + impl_.retrieve( + probing_cg, k, flushing_cg_counter, output_buffer, num_matches, output_begin, key_equal); + } +} + +template +template +__device__ __forceinline__ void +static_multimap::device_view::retrieve_outer( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + Key const& k, + uint32_t* flushing_cg_counter, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin, + KeyEqual key_equal) noexcept +{ + constexpr bool is_outer = true; + if constexpr (uses_vector_load()) { + impl_.retrieve(flushing_cg, + probing_cg, + k, + flushing_cg_counter, + output_buffer, + num_matches, + output_begin, + key_equal); + } else // In the case of scalar load, flushing CG is the same as probing CG + { + impl_.retrieve( + probing_cg, k, flushing_cg_counter, output_buffer, num_matches, output_begin, key_equal); + } +} + +template +template +__device__ __forceinline__ void +static_multimap::device_view::pair_retrieve( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + value_type const& pair, + uint32_t* flushing_cg_counter, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal) noexcept +{ + constexpr bool is_outer = false; + if constexpr (uses_vector_load()) { + impl_.pair_retrieve(flushing_cg, + probing_cg, + pair, + flushing_cg_counter, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin, + pair_equal); + } else // In the case of scalar load, flushing CG is the same as probing CG + { + impl_.pair_retrieve(probing_cg, + pair, + flushing_cg_counter, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin, + pair_equal); + } +} + +template +template +__device__ __forceinline__ void +static_multimap::device_view::pair_retrieve_outer( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + value_type const& pair, + uint32_t* flushing_cg_counter, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal) noexcept +{ + constexpr bool is_outer = true; + if constexpr (uses_vector_load()) { + impl_.pair_retrieve(flushing_cg, + probing_cg, + pair, + flushing_cg_counter, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin, + pair_equal); + } else // In the case of scalar load, flushing CG is the same as probing CG + { + impl_.pair_retrieve(probing_cg, + pair, + flushing_cg_counter, + probe_output_buffer, + contained_output_buffer, + num_matches, + probe_output_begin, + contained_output_begin, + pair_equal); + } +} + +template +std::size_t static_multimap::get_size( + cudaStream_t stream) const noexcept +{ + auto begin = thrust::make_transform_iterator( + raw_slots(), [] __device__(cuco::pair_type const& pair) { return pair.first; }); + slot_is_filled filled(empty_key_sentinel_); + + return thrust::count_if(thrust::cuda::par.on(stream), begin, begin + capacity_, filled); +} + +template +float static_multimap::get_load_factor( + cudaStream_t stream) const noexcept +{ + auto size = get_size(stream); + return static_cast(size) / static_cast(capacity_); +} + +} // namespace cuco diff --git a/include/cuco/detail/util.hpp b/include/cuco/detail/util.hpp new file mode 100644 index 000000000..40697ff5c --- /dev/null +++ b/include/cuco/detail/util.hpp @@ -0,0 +1,53 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + */ + +#pragma once + +namespace cuco { +namespace detail { + +/** + * @brief Compute the number of bits of a simple type. + * + * @tparam T The type we want to infer its size in bits + * + * @return Size of type T in bits + */ +template +static constexpr std::size_t type_bits() noexcept +{ + return sizeof(T) * CHAR_BIT; +} + +// safe division +#ifndef SDIV +#define SDIV(x, y) (((x) + (y)-1) / (y)) +#endif + +template +auto get_grid_size(Kernel kernel, std::size_t block_size, std::size_t dynamic_smem_bytes = 0) +{ + int grid_size{-1}; + cudaOccupancyMaxActiveBlocksPerMultiprocessor(&grid_size, kernel, block_size, dynamic_smem_bytes); + int dev_id{-1}; + cudaGetDevice(&dev_id); + int num_sms{-1}; + cudaDeviceGetAttribute(&num_sms, cudaDevAttrMultiProcessorCount, dev_id); + grid_size *= num_sms; + return grid_size; +} + +} // namespace detail +} // namespace cuco diff --git a/include/cuco/probe_sequences.cuh b/include/cuco/probe_sequences.cuh new file mode 100644 index 000000000..f923f9df6 --- /dev/null +++ b/include/cuco/probe_sequences.cuh @@ -0,0 +1,72 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +namespace cuco { + +/** + * @brief Public linear probing scheme class. + * + * Linear probing is efficient when few collisions are present. Performance hints: + * - Use linear probing when collisions are rare. e.g. low occupancy or low multiplicity. + * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise. + * + * `Hash` should be callable object type. + * + * @tparam CGSize Size of CUDA Cooperative Groups + * @tparam Hash Unary callable type + */ +template +class linear_probing : public detail::probe_sequence_base { + public: + using probe_sequence_base_type = detail::probe_sequence_base; + using probe_sequence_base_type::cg_size; + using probe_sequence_base_type::vector_width; + + template + using impl = detail::linear_probing_impl; +}; + +/** + * + * @brief Public double hashing scheme class. + * + * Default probe sequence for `cuco::static_multimap`. Double hashing shows superior + * performance when dealing with high multiplicty and/or high occupancy use cases. Performance + * hints: + * - `CGSize` = 1 or 2 when hash map is small (10'000'000 or less), 4 or 8 otherwise. + * + * `Hash1` and `Hash2` should be callable object type. + * + * @tparam CGSize Size of CUDA Cooperative Groups + * @tparam Hash1 Unary callable type + * @tparam Hash2 Unary callable type + */ +template +class double_hashing : public detail::probe_sequence_base { + public: + using probe_sequence_base_type = detail::probe_sequence_base; + using probe_sequence_base_type::cg_size; + using probe_sequence_base_type::vector_width; + + template + using impl = detail::double_hashing_impl; +}; + +} // namespace cuco diff --git a/include/cuco/static_map.cuh b/include/cuco/static_map.cuh index 5fbc00cea..582549de3 100644 --- a/include/cuco/static_map.cuh +++ b/include/cuco/static_map.cuh @@ -24,6 +24,7 @@ #include #include +#include #if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \ (__CUDA_ARCH__ >= 700) @@ -44,41 +45,6 @@ namespace cuco { template class dynamic_map; -/** - * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise - * equality comparisons on objects of type `T`. - * - * By default, only types where `std::has_unique_object_representations_v` is true are safe for - * bitwise equality. However, this can be too restrictive for some types, e.g., floating point - * types. - * - * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users - * responsibility to ensure values do not occur that would lead to unexpected behavior. For example, - * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to - * other `NaN` bit patterns. - * - */ -template -struct is_bitwise_comparable : std::false_type { -}; - -/// By default, only types with unique object representations are allowed -template -struct is_bitwise_comparable>> - : std::true_type { -}; - -/** - * @brief Declares that a type `Type` is bitwise comparable. - * - */ -#define CUCO_DECLARE_BITWISE_COMPARABLE(Type) \ - namespace cuco { \ - template <> \ - struct is_bitwise_comparable : std::true_type { \ - }; \ - } - /** * @brief A GPU-accelerated, unordered, associative container of key-value * pairs with unique keys. @@ -88,7 +54,7 @@ struct is_bitwise_comparable::value` is true + * - Requires keys and values that where `cuco::is_bitwise_comparable_v` is true * - Comparisons against the "sentinel" values will always be done with bitwise comparisons. * - Does not support erasing keys * - Capacity is fixed and will not grow automatically @@ -153,14 +119,14 @@ template > class static_map { static_assert( - is_bitwise_comparable::value, + cuco::is_bitwise_comparable_v, "Key type must have unique object representations or have been explicitly declared as safe for " - "bitwise comparison via specialization of cuco::is_bitwise_comparable."); + "bitwise comparison via specialization of cuco::is_bitwise_comparable_v."); - static_assert(is_bitwise_comparable::value, + static_assert(cuco::is_bitwise_comparable_v, "Value type must have unique object representations or have been explicitly " "declared as safe for bitwise comparison via specialization of " - "cuco::is_bitwise_comparable."); + "cuco::is_bitwise_comparable_v."); friend class dynamic_map; diff --git a/include/cuco/static_multimap.cuh b/include/cuco/static_multimap.cuh new file mode 100644 index 000000000..9261ef9a2 --- /dev/null +++ b/include/cuco/static_multimap.cuh @@ -0,0 +1,1159 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include +#include +#include + +#include +#include +#include + +#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11000) && defined(__CUDA_ARCH__) && \ + (__CUDA_ARCH__ >= 700) +#define CUCO_HAS_CUDA_BARRIER +#endif + +// cg::memcpy_aysnc is supported for CUDA 11.1 and up +#if defined(CUDART_VERSION) && (CUDART_VERSION >= 11100) +#define CUCO_HAS_CG_MEMCPY_ASYNC +#endif + +#if defined(CUCO_HAS_CUDA_BARRIER) +#include +#endif + +#include +#include +#include + +namespace cuco { + +/** + * @brief A GPU-accelerated, unordered, associative container of key-value + * pairs that supports equivalent keys. + * + * Allows constant time concurrent inserts or concurrent find operations from threads in device + * code. Concurrent insert/find is allowed only when + * `static_multimap::supports_concurrent_insert_find()` is true. + * + * Current limitations: + * - Requires keys and values where `cuco::is_bitwise_comparable_v` is true + * - Comparisons against the "sentinel" values will always be done with bitwise comparisons + * Therefore, the objects must have unique, bitwise object representations (e.g., no padding bits). + * - Does not support erasing keys + * - Capacity is fixed and will not grow automatically + * - Requires the user to specify sentinel values for both key and mapped value + * to indicate empty slots + * - Concurrent insert/find is only supported when `static_multimap::supports_concurrent_insert_find()` is true` + * + * The `static_multimap` supports two types of operations: + * - Host-side "bulk" operations + * - Device-side "singular" operations + * + * The host-side bulk operations include `insert`, `contains`, `count`, `retrieve` and their + * variants. These APIs should be used when there are a large number of keys to insert or lookup in + * the map. For example, given a range of keys specified by device-accessible iterators, the bulk + * `insert` function will insert all keys into the map. + * + * The singular device-side operations allow individual threads to perform + * independent operations (e.g. `insert`, etc.) from device code. These + * operations are accessed through non-owning, trivially copyable "view" types: + * `device_view` and `device_mutable_view`. The `device_view` class is an + * immutable view that allows only non-modifying operations such as `count` or + * `contains`. The `device_mutable_view` class only allows `insert` operations. + * The two types are separate to prevent erroneous concurrent insert/find + * operations. + * + * By default, when querying for a Key `k` in operations like `count` or `retrieve`, if `k` is not + * present in the map, it will not contribute to the output. Query APIs with the `_outer` suffix + * will include non-matching keys in the output. See the relevant API documentation for more + * information. + * + * Typical associative container query APIs like `retrieve` look up values by solely by key, e.g., + * `count` for a Key `k` will count all values whose associated key `k'` matches `k` as determined + * by `key_equal(k, k')`. In some cases, one may want to consider both key _and_ value when + * determining if a key-value pair should contribute to the output. `static_multimap` supports this + * use case with APIs prefixed with `pair_`, e.g., `pair_count` is given a key-value pair + * `{k,v}` and only counts key-value pairs, `{k', v'}`, in the map where `pair_equal({k,v}, {k', + * v'})` is true. See the relevant API documentation for more information. + * + * Example: + * \code{.cpp} + * int empty_key_sentinel = -1; + * int empty_value_sentinel = -1; + * + * // Constructs a multimap with 100,000 slots using -1 and -1 as the empty key/value + * // sentinels. Note the capacity is chosen knowing we will insert 50,000 keys, + * // for an load factor of 50%. + * static_multimap m{100'000, empty_key_sentinel, empty_value_sentinel}; + * + * // Create a sequence of pairs {{0,0}, {1,1}, ... {i,i}} + * thrust::device_vector> pairs(50,000); + * thrust::transform(thrust::make_counting_iterator(0), + * thrust::make_counting_iterator(pairs.size()), + * pairs.begin(), + * []__device__(auto i){ return thrust::make_pair(i,i); }; + * + * + * // Inserts all pairs into the map + * m.insert(pairs.begin(), pairs.end()); + * + * // Get a `device_view` and passes it to a kernel where threads may perform + * // `contains/count/retrieve` lookups + * kernel<<<...>>>(m.get_device_view()); + * \endcode + * + * + * @tparam Key Type used for keys. Requires `cuco::is_bitwise_comparable_v` + * @tparam Value Type of the mapped values. Requires `cuco::is_bitwise_comparable_v` + * @tparam Scope The scope in which multimap operations will be performed by + * individual threads + * @tparam ProbeSequence Probe sequence chosen between `cuco::detail::linear_probing` + * and `cuco::detail::double_hashing`. (see `detail/probe_sequences.cuh`) + * @tparam Allocator Type of allocator used for device storage + */ +template , + class ProbeSequence = + cuco::double_hashing<8, detail::MurmurHash3_32, detail::MurmurHash3_32>> +class static_multimap { + static_assert( + cuco::is_bitwise_comparable_v, + "Key type must have unique object representations or have been explicitly declared as safe for " + "bitwise comparison via specialization of cuco::is_bitwise_comparable_v."); + + static_assert( + cuco::is_bitwise_comparable_v, + "Value type must have unique object representations or have been explicitly declared as safe " + "for bitwise comparison via specialization of cuco::is_bitwise_comparable_v."); + + static_assert( + std::is_base_of_v, ProbeSequence>, + "ProbeSequence must be a specialization of either cuco::double_hashing or " + "cuco::linear_probing"); + + public: + using value_type = cuco::pair_type; + using key_type = Key; + using mapped_type = Value; + using atomic_key_type = cuda::atomic; + using atomic_mapped_type = cuda::atomic; + using pair_atomic_type = cuco::pair_type; + using atomic_ctr_type = cuda::atomic; + using allocator_type = Allocator; + using slot_allocator_type = + typename std::allocator_traits::rebind_alloc; + using counter_allocator_type = + typename std::allocator_traits::rebind_alloc; + using probe_sequence_type = detail::probe_sequence; + + static_multimap(static_multimap const&) = delete; + static_multimap& operator=(static_multimap const&) = delete; + + static_multimap(static_multimap&&) = default; + static_multimap& operator=(static_multimap&&) = default; + ~static_multimap() = default; + + /** + * @brief Indicate if concurrent insert/find is supported for the key/value types. + * + * @return Boolean indicating if concurrent insert/find is supported. + */ + __host__ __device__ __forceinline__ static constexpr bool + supports_concurrent_insert_find() noexcept + { + return cuco::detail::is_packable(); + } + + /** + * @brief The size of the CUDA cooperative thread group. + * + * @return The CG size. + */ + __host__ __device__ __forceinline__ static constexpr uint32_t cg_size() noexcept + { + return ProbeSequence::cg_size; + } + + /** + * @brief Construct a statically-sized map with the specified initial capacity, + * sentinel values and CUDA stream. + * + * The capacity of the map is fixed. Insert operations will not automatically + * grow the map. Attempting to insert more unique keys than the capacity of + * the map results in undefined behavior. + * + * Performance begins to degrade significantly beyond a load factor of ~70%. + * For best performance, choose a capacity that will keep the load factor + * below 70%. E.g., if inserting `N` unique keys, choose a capacity of + * `N * (1/0.7)`. + * + * The `empty_key_sentinel` and `empty_value_sentinel` values are reserved and + * undefined behavior results from attempting to insert any key/value pair + * that contains either. + * + * @param capacity The total number of slots in the map + * @param empty_key_sentinel The reserved key value for empty slots + * @param empty_value_sentinel The reserved mapped value for empty slots + * @param stream CUDA stream used to initialize the map + * @param alloc Allocator used for allocating device storage + */ + static_multimap(std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel, + cudaStream_t stream = 0, + Allocator const& alloc = Allocator{}); + + /** + * @brief Inserts all key/value pairs in the range `[first, last)`. + * + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @param first Beginning of the sequence of key/value pairs + * @param last End of the sequence of key/value pairs + * @param stream CUDA stream used for insert + */ + template + void insert(InputIt first, InputIt last, cudaStream_t stream = 0); + + /** + * @brief Inserts key/value pairs in the range `[first, first + n)` if `pred` + * of the corresponding stencil returns true. + * + * The key/value pair `*(first + i)` is inserted if `pred( *(stencil + i) )` returns true. + * + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam StencilIt Device accessible random access iterator whose value_type is + * convertible to Predicate's argument type + * @tparam Predicate Unary predicate callable whose return type must be convertible to `bool` and + * argument type is convertible from `std::iterator_traits::value_type`. + * @param first Beginning of the sequence of key/value pairs + * @param last End of the sequence of key/value pairs + * @param stencil Beginning of the stencil sequence + * @param pred Predicate to test on every element in the range `[stencil, stencil + + * std::distance(first, last))` + * @param stream CUDA stream used for insert + */ + template + void insert_if( + InputIt first, InputIt last, StencilIt stencil, Predicate pred, cudaStream_t stream = 0); + + /** + * @brief Indicates whether the keys in the range `[first, last)` are contained in the map. + * + * Stores `true` or `false` to `(output + i)` indicating if the key `*(first + i)` exists in the + * map. + * + * @tparam InputIt Device accessible input iterator whose `value_type` is + * convertible to the map's `key_type` + * @tparam OutputIt Device accessible output iterator whose `value_type` is convertible from + * `bool` + * @tparam KeyEqual Binary callable type used to compare two keys for equality + * @param first Beginning of the sequence of keys + * @param last End of the sequence of keys + * @param output_begin Beginning of the output sequence indicating whether each key is present + * @param stream CUDA stream used for contains + * @param key_equal The binary function to compare two keys for equality + */ + template > + void contains(InputIt first, + InputIt last, + OutputIt output_begin, + cudaStream_t stream = 0, + KeyEqual key_equal = KeyEqual{}) const; + + /** + * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. + * + * For each key, `k = *(first + i)`, counts all matching keys, `k'`, as determined by + * `key_equal(k, k')` and returns the sum of all matches for all keys. + * + * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type` + * @tparam KeyEqual Binary callable + * @param first Beginning of the sequence of keys to count + * @param last End of the sequence of keys to count + * @param stream CUDA stream used for count + * @param key_equal Binary function to compare two keys for equality + * @return The sum of total occurrences of all keys in `[first, last)` + */ + template > + std::size_t count(InputIt first, + InputIt last, + cudaStream_t stream = 0, + KeyEqual key_equal = KeyEqual{}) const; + + /** + * @brief Counts the occurrences of keys in `[first, last)` contained in the multimap. + * + * For each key, `k = *(first + i)`, counts all matching keys, `k'`, as determined by + * `key_equal(k, k')` and returns the sum of all matches for all keys. If `k` does not have any + * matches, it contributes 1 to the final sum. + * + * @tparam Input Device accesible input iterator whose `value_type` is convertible to `key_type` + * @tparam KeyEqual Binary callable + * @param first Beginning of the sequence of keys to count + * @param last End of the sequence of keys to count + * @param stream CUDA stream used for count_outer + * @param key_equal Binary function to compare two keys for equality + * @return The sum of total occurrences of all keys in `[first, last)` where keys without matches + * are considered to have a single occurrence. + */ + template > + std::size_t count_outer(InputIt first, + InputIt last, + cudaStream_t stream = 0, + KeyEqual key_equal = KeyEqual{}) const; + + /** + * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap. + * + * For key-value pair, `kv = *(first + i)`, counts all matching key-value pairs, `kv'`, as + * determined by `pair_equal(kv, kv')` and returns the sum of all matches for all key-value pairs. + * + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam PairEqual Binary callable + * @param first Beginning of the sequence of pairs to count + * @param last End of the sequence of pairs to count + * @param pair_equal Binary function to compare two pairs for equality + * @param stream CUDA stream used for pair_count + * @return The sum of total occurrences of all pairs in `[first, last)` + */ + template + std::size_t pair_count(InputIt first, + InputIt last, + PairEqual pair_equal, + cudaStream_t stream = 0) const; + + /** + * @brief Counts the occurrences of key/value pairs in `[first, last)` contained in the multimap. + * + * For key-value pair, `kv = *(first + i)`, counts all matching key-value pairs, `kv'`, as + * determined by `pair_equal(kv, kv')` and returns the sum of all matches for all key-value pairs. + * if `kv` does not have any matches, it contributes 1 to the final sum. + * + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam PairEqual Binary callable + * @param first Beginning of the sequence of pairs to count + * @param last End of the sequence of pairs to count + * @param pair_equal Binary function to compare two pairs for equality + * @param stream CUDA stream used for pair_count_outer + * @return The sum of total occurrences of all pairs in `[first, last)` where a key-value pair + * without a match is considered to have a single occurrence + */ + template + std::size_t pair_count_outer(InputIt first, + InputIt last, + PairEqual pair_equal, + cudaStream_t stream = 0) const; + + /** + * @brief Retrieves all the values corresponding to all keys in the range `[first, last)`. + * + * If key `k = *(first + i)` exists in the map, copies `k` and all associated values to + * unspecified locations in `[output_begin, output_end)`. Else, does nothing. + * + * Behavior is undefined if the size of the output range exceeds `std::distance(output_begin, + * output_end)`. Use `count()` to determine the size of the output range. + * + * @tparam InputIt Device accessible input iterator whose `value_type` is + * convertible to the map's `key_type` + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam KeyEqual Binary callable type + * @param first Beginning of the sequence of keys + * @param last End of the sequence of keys + * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key + * @param stream CUDA stream used for retrieve + * @param key_equal The binary function to compare two keys for equality + * @return The iterator indicating the last valid key/value pairs in the output + */ + template > + OutputIt retrieve(InputIt first, + InputIt last, + OutputIt output_begin, + cudaStream_t stream = 0, + KeyEqual key_equal = KeyEqual{}) const; + + /** + * @brief Retrieves all the matches corresponding to all keys in the range `[first, last)`. + * + * If key `k = *(first + i)` exists in the map, copies `k` and all associated values to + * unspecified locations in `[output_begin, output_end)`. Else, copies `k` and + * `empty_value_sentinel`. + * + * Behavior is undefined if the size of the output range exceeds `std::distance(output_begin, + * output_end)`. Use `count_outer()` to determine the size of the output range. + * + * @tparam InputIt Device accessible input iterator whose `value_type` is + * convertible to the map's `key_type` + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam KeyEqual Binary callable type + * @param first Beginning of the sequence of keys + * @param last End of the sequence of keys + * @param output_begin Beginning of the sequence of key/value pairs retrieved for each key + * @param stream CUDA stream used for retrieve_outer + * @param key_equal The binary function to compare two keys for equality + * @return The iterator indicating the last valid key/value pairs in the output + */ + template > + OutputIt retrieve_outer(InputIt first, + InputIt last, + OutputIt output_begin, + cudaStream_t stream = 0, + KeyEqual key_equal = KeyEqual{}) const; + + /** + * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`. + * + * The `pair_` prefix indicates that the input data type is convertible to the map's + * `value_type`. If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is + * stored to `probe_output_begin`, and slot[j] is stored to `contained_output_begin`. + * + * Behavior is undefined if the size of the output range exceeds + * `std::distance(probe_output_begin, probe_output_end)` (or + * `std::distance(contained_output_begin, contained_output_end)`). Use + * `pair_count()` to determine the size of the output range. + * + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam PairEqual Binary callable type + * @param first Beginning of the sequence of pairs + * @param last End of the sequence of pairs + * @param probe_output_begin Beginning of the sequence of the matched probe pairs + * @param contained_output_begin Beginning of the sequence of the matched contained pairs + * @param pair_equal The binary function to compare two pairs for equality + * @param stream CUDA stream used for pair_retrieve + * @return Pair of iterators pointing to the last elements in the output + */ + template + std::pair pair_retrieve(InputIt first, + InputIt last, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal, + cudaStream_t stream = 0) const; + + /** + * @brief Retrieves all pairs matching the input probe pair in the range `[first, last)`. + * + * The `pair_` prefix indicates that the input data type is convertible to the map's `value_type`. + * If pair_equal(*(first + i), slot[j]) returns true, then *(first+i) is stored to + * `probe_output_begin`, and slot[j] is stored to `contained_output_begin`. If *(first+i) doesn't + * have matches in the map, copies *(first + i) in `probe_output_begin` and a pair of + * `empty_key_sentinel` and `empty_value_sentinel` in `contained_output_begin`. + * + * Behavior is undefined if the size of the output range exceeds + * `std::distance(probe_output_begin, probe_output_end)` (or + * `std::distance(contained_output_begin, contained_output_end)`). Use + * `pair_count()` to determine the size of the output range. + * + * @tparam InputIt Device accessible random access input iterator where + * `std::is_convertible::value_type, + * static_multimap::value_type>` is `true` + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam PairEqual Binary callable type + * @param first Beginning of the sequence of pairs + * @param last End of the sequence of pairs + * @param probe_output_begin Beginning of the sequence of the matched probe pairs + * @param contained_output_begin Beginning of the sequence of the matched contained pairs + * @param pair_equal The binary function to compare two pairs for equality + * @param stream CUDA stream used for pair_retrieve_outer + * @return Pair of iterators pointing to the last elements in the output + */ + template + std::pair pair_retrieve_outer(InputIt first, + InputIt last, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal, + cudaStream_t stream = 0) const; + + private: + /** + * @brief Indicates if vector-load is used. + * + * Users have no explicit control on whether vector-load is used. + * + * @return Boolean indicating if vector-load is used. + */ + static constexpr bool uses_vector_load() noexcept + { + return cuco::detail::is_packable(); + } + + /** + * @brief Returns the number of pairs loaded with each vector-load + */ + static constexpr uint32_t vector_width() noexcept { return ProbeSequence::vector_width(); } + + /** + * @brief Returns the warp size. + */ + static constexpr uint32_t warp_size() noexcept { return 32u; } + + /** + * @brief Custom deleter for unique pointer of device counter. + */ + struct counter_deleter { + counter_deleter(counter_allocator_type& a, cudaStream_t& s) : allocator{a}, stream{s} {} + + counter_deleter(counter_deleter const&) = default; + + void operator()(atomic_ctr_type* ptr) { allocator.deallocate(ptr, 1, stream); } + + counter_allocator_type& allocator; + cudaStream_t& stream; + }; + + /** + * @brief Custom deleter for unique pointer of slots. + */ + struct slot_deleter { + slot_deleter(slot_allocator_type& a, size_t& c, cudaStream_t& s) + : allocator{a}, capacity{c}, stream{s} + { + } + + slot_deleter(slot_deleter const&) = default; + + void operator()(pair_atomic_type* ptr) { allocator.deallocate(ptr, capacity, stream); } + + slot_allocator_type& allocator; + size_t& capacity; + cudaStream_t& stream; + }; + + class device_view_impl_base; + class device_mutable_view_impl; + class device_view_impl; + + public: + /** + * @brief Mutable, non-owning view-type that may be used in device code to + * perform singular inserts into the map. + * + * `device_mutable_view` is trivially-copyable and is intended to be passed by + * value. + * + * Example: + * \code{.cpp} + * cuco::static_multimap m{100'000, -1, -1}; + * + * // Inserts a sequence of pairs {{0,0}, {1,1}, ... {i,i}} + * thrust::for_each(thrust::make_counting_iterator(0), + * thrust::make_counting_iterator(50'000), + * [map = m.get_device_mutable_view()] + * __device__ (auto i) mutable { + * map.insert(thrust::make_pair(i,i)); + * }); + * \endcode + */ + class device_mutable_view { + public: + // Import member type definitions from `static_multimap` + using value_type = value_type; + using key_type = Key; + using mapped_type = Value; + using iterator = pair_atomic_type*; + using const_iterator = pair_atomic_type const*; + + /** + * @brief Construct a mutable view of the first `capacity` slots of the + * slots array pointed to by `slots`. + * + * @param slots Pointer to beginning of initialized slots array + * @param capacity The number of slots viewed by this object + * @param empty_key_sentinel The reserved value for keys to represent empty + * slots + * @param empty_value_sentinel The reserved value for mapped values to + * represent empty slots + */ + __host__ __device__ device_mutable_view(pair_atomic_type* slots, + std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel) noexcept + : impl_{slots, capacity, empty_key_sentinel, empty_value_sentinel} + { + } + + /** + * @brief Inserts the specified key/value pair into the map. + * + * @param g The Cooperative Group that performs the insert + * @param insert_pair The pair to insert + * @return void. + */ + __device__ __forceinline__ void insert( + cooperative_groups::thread_block_tile const& g, + value_type const& insert_pair) noexcept; + + private: + device_mutable_view_impl impl_; + }; // class device mutable view + + /** + * @brief Non-owning view-type that may be used in device code to + * perform singular find and contains operations for the map. + * + * `device_view` is trivially-copyable and is intended to be passed by + * value. + * + */ + class device_view { + public: + // Import member type definitions from `static_multimap` + using value_type = value_type; + using key_type = Key; + using mapped_type = Value; + using iterator = pair_atomic_type*; + using const_iterator = pair_atomic_type const*; + + /** + * @brief Construct a view of the first `capacity` slots of the + * slots array pointed to by `slots`. + * + * @param slots Pointer to beginning of initialized slots array + * @param capacity The number of slots viewed by this object + * @param empty_key_sentinel The reserved value for keys to represent empty + * slots + * @param empty_value_sentinel The reserved value for mapped values to + * represent empty slots + */ + __host__ __device__ device_view(pair_atomic_type* slots, + std::size_t capacity, + Key empty_key_sentinel, + Value empty_value_sentinel) noexcept + : impl_{slots, capacity, empty_key_sentinel, empty_value_sentinel} + { + } + + /** + * @brief Gets slots array. + * + * @return Slots array + */ + __device__ __forceinline__ pair_atomic_type* get_slots() noexcept { return impl_.get_slots(); } + + /** + * @brief Gets slots array. + * + * @return Slots array + */ + __device__ __forceinline__ pair_atomic_type const* get_slots() const noexcept + { + return impl_.get_slots(); + } + + /** + * @brief Gets the maximum number of elements the hash map can hold. + * + * @return The maximum number of elements the hash map can hold + */ + __host__ __device__ __forceinline__ std::size_t get_capacity() const noexcept + { + return impl_.get_capacity(); + } + + /** + * @brief Gets the sentinel value used to represent an empty key slot. + * + * @return The sentinel value used to represent an empty key slot + */ + __host__ __device__ __forceinline__ Key get_empty_key_sentinel() const noexcept + { + return impl_.get_empty_key_sentinel(); + } + + /** + * @brief Gets the sentinel value used to represent an empty value slot. + * + * @return The sentinel value used to represent an empty value slot + */ + __host__ __device__ __forceinline__ Value get_empty_value_sentinel() const noexcept + { + return impl_.get_empty_value_sentinel(); + } + + /** + * @brief Makes a copy of given `device_view` using non-owned memory. + * + * This function is intended to be used to create shared memory copies of small static maps, + * although global memory can be used as well. + * + * @tparam CG The type of the cooperative thread group + * @param g The cooperative thread group used to copy the slots + * @param source_device_view `device_view` to copy from + * @param memory_to_use Array large enough to support `capacity` elements. Object does not + * take the ownership of the memory + * @return Copy of passed `device_view` + */ + template + __device__ __forceinline__ static device_view make_copy( + CG g, pair_atomic_type* const memory_to_use, device_view source_device_view) noexcept; + + /** + * @brief Flushes per-CG buffer into the output sequence. + * + * A given CUDA Cooperative Group, `g`, loads `num_outputs` key-value pairs from `output_buffer` + * and writes them into global memory in a coalesced fashion. CG-wide `memcpy_sync` is used if + * `CUCO_HAS_CG_MEMCPY_ASYNC` is defined and `thrust::is_contiguous_iterator_v` + * returns true. All threads of `g` must be active due to implicit CG-wide synchronization + * during flushing. + * + * @tparam CG Cooperative Group type + * @tparam atomicT Type of atomic storage + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @param g The Cooperative Group used to flush output buffer + * @param num_outputs Number of valid output in the buffer + * @param output_buffer Buffer of the key/value pair sequence + * @param num_matches Size of the output sequence + * @param output_begin Beginning of the output sequence of key/value pairs + */ + template + __device__ __forceinline__ void flush_output_buffer(CG const& g, + uint32_t const num_outputs, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin) noexcept; + + /** + * @brief Flushes per-CG buffer into the output sequences. + * + * A given CUDA Cooperative Group, `g`, loads `num_outputs` elements from `probe_output_buffer` + * and `num_outputs` elements from `contained_output_buffer`, then writes them into global + * memory started from `probe_output_begin` and `contained_output_begin` respectively. All + * threads of `g` must be active due to implicit CG-wide synchronization during flushing. + * + * @tparam CG Cooperative Group type + * @tparam atomicT Type of atomic storage + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @param g The Cooperative Group used to flush output buffer + * @param num_outputs Number of valid output in the buffer + * @param probe_output_buffer Buffer of the matched probe pair sequence + * @param contained_output_buffer Buffer of the matched contained pair sequence + * @param num_matches Size of the output sequence + * @param probe_output_begin Beginning of the output sequence of the matched probe pairs + * @param contained_output_begin Beginning of the output sequence of the matched contained + * pairs + */ + template + __device__ __forceinline__ void flush_output_buffer(CG const& g, + uint32_t const num_outputs, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin) noexcept; + + /** + * @brief Indicates whether the key `k` exists in the map. + * + * If the key `k` was inserted into the map, `contains` returns + * true. Otherwise, it returns false. Uses the CUDA Cooperative Groups API to + * to leverage multiple threads to perform a single `contains` operation. This provides a + * significant boost in throughput compared to the non Cooperative Group + * `contains` at moderate to high load factors. + * + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the contains operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return A boolean indicating whether the key/value pair + * containing `k` was inserted + */ + template > + __device__ __forceinline__ bool contains( + cooperative_groups::thread_block_tile const& g, + Key const& k, + KeyEqual key_equal = KeyEqual{}) noexcept; + + /** + * @brief Counts the occurrence of a given key contained in multimap. + * + * For a given key, `k`, counts all matching keys, `k'`, as determined by `key_equal(k, k')` and + * returns the sum of all matches for `k`. + * + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the count operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return Number of matches found by the current thread + */ + template > + __device__ __forceinline__ std::size_t count( + cooperative_groups::thread_block_tile const& g, + Key const& k, + KeyEqual key_equal = KeyEqual{}) noexcept; + + /** + * @brief Counts the occurrence of a given key contained in multimap. If no + * matches can be found for a given key, the corresponding occurrence is 1. + * + * For a given key, `k`, counts all matching keys, `k'`, as determined by `key_equal(k, k')` and + * returns the sum of all matches for `k`. If `k` does not have any matches, returns 1. + * + * @tparam KeyEqual Binary callable type + * @param g The Cooperative Group used to perform the count operation + * @param k The key to search for + * @param key_equal The binary callable used to compare two keys + * for equality + * @return Number of matches found by the current thread + */ + template > + __device__ __forceinline__ std::size_t count_outer( + cooperative_groups::thread_block_tile const& g, + Key const& k, + KeyEqual key_equal = KeyEqual{}) noexcept; + + /** + * @brief Counts the occurrence of a given key/value pair contained in multimap. + * + * For a given pair, `p`, counts all matching pairs, `p'`, as determined by `pair_equal(p, p')` + * and returns the sum of all matches for `p`. + * + * @tparam PairEqual Binary callable type + * @param g The Cooperative Group used to perform the pair_count operation + * @param pair The pair to search for + * @param pair_equal The binary callable used to compare two pairs + * for equality + * @return Number of matches found by the current thread + */ + template + __device__ __forceinline__ std::size_t pair_count( + cooperative_groups::thread_block_tile const& g, + value_type const& pair, + PairEqual pair_equal) noexcept; + + /** + * @brief Counts the occurrence of a given key/value pair contained in multimap. + * If no matches can be found for a given key, the corresponding occurrence is 1. + * + * For a given pair, `p`, counts all matching pairs, `p'`, as determined by `pair_equal(p, p')` + * and returns the sum of all matches for `p`. If `p` does not have any matches, returns 1. + * + * @tparam PairEqual Binary callable type + * @param g The Cooperative Group used to perform the pair_count operation + * @param pair The pair to search for + * @param pair_equal The binary callable used to compare two pairs + * for equality + * @return Number of matches found by the current thread + */ + template + __device__ __forceinline__ std::size_t pair_count_outer( + cooperative_groups::thread_block_tile const& g, + value_type const& pair, + PairEqual pair_equal) noexcept; + + /** + * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG + * shared memory buffer. + * + * For key `k` existing in the map, copies `k` and all associated values to unspecified + * locations in `[output_begin, output_end)`. + * + * @tparam buffer_size Size of the output buffer + * @tparam FlushingCG Type of Cooperative Group used to flush output buffer + * @tparam atomicT Type of atomic storage + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam KeyEqual Binary callable type + * @param flushing_cg The Cooperative Group used to flush output buffer + * @param probing_cg The Cooperative Group used to retrieve + * @param k The key to search for + * @param flushing_cg_counter Pointer to flushing_cg counter + * @param output_buffer Shared memory buffer of the key/value pair sequence + * @param num_matches Size of the output sequence + * @param output_begin Beginning of the output sequence of key/value pairs + * @param key_equal The binary callable used to compare two keys + * for equality + */ + template > + __device__ __forceinline__ void retrieve( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + Key const& k, + uint32_t* flushing_cg_counter, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin, + KeyEqual key_equal = KeyEqual{}) noexcept; + + /** + * @brief Retrieves all the matches of a given key contained in multimap with per-flushing-CG + * shared memory buffer. + * + * For key `k` existing in the map, copies `k` and all associated values to unspecified + * locations in `[output_begin, output_end)`. If `k` does not have any matches, copies `k` and + * `empty_value_sentinel()` into the output. + * + * @tparam buffer_size Size of the output buffer + * @tparam FlushingCG Type of Cooperative Group used to flush output buffer + * @tparam atomicT Type of atomic storage + * @tparam OutputIt Device accessible output iterator whose `value_type` is + * constructible from the map's `value_type` + * @tparam KeyEqual Binary callable type + * @param flushing_cg The Cooperative Group used to flush output buffer + * @param probing_cg The Cooperative Group used to retrieve + * @param k The key to search for + * @param flushing_cg_counter Pointer to flushing_cg counter + * @param output_buffer Shared memory buffer of the key/value pair sequence + * @param num_matches Size of the output sequence + * @param output_begin Beginning of the output sequence of key/value pairs + * @param key_equal The binary callable used to compare two keys + * for equality + */ + template > + __device__ __forceinline__ void retrieve_outer( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + Key const& k, + uint32_t* flushing_cg_counter, + value_type* output_buffer, + atomicT* num_matches, + OutputIt output_begin, + KeyEqual key_equal = KeyEqual{}) noexcept; + + /** + * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG + * shared memory buffer. + * + * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations + * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in + * `[contained_output_begin, contained_output_end)`. + * + * @tparam buffer_size Size of the output buffer + * @tparam FlushingCG Type of Cooperative Group used to flush output buffer + * @tparam atomicT Type of atomic storage + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam PairEqual Binary callable type + * @param flushing_cg The Cooperative Group used to flush output buffer + * @param probing_cg The Cooperative Group used to retrieve + * @param pair The pair to search for + * @param flushing_cg_counter Pointer to the flushing CG counter + * @param probe_output_buffer Buffer of the matched probe pair sequence + * @param contained_output_buffer Buffer of the matched contained pair sequence + * @param num_matches Size of the output sequence + * @param probe_output_begin Beginning of the output sequence of the matched probe pairs + * @param contained_output_begin Beginning of the output sequence of the matched contained + * pairs + * @param pair_equal The binary callable used to compare two pairs for equality + */ + template + __device__ __forceinline__ void pair_retrieve( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + value_type const& pair, + uint32_t* warp_counter, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal) noexcept; + + /** + * @brief Retrieves all the matches of a given pair contained in multimap with per-flushing-CG + * shared memory buffer. + * + * For pair `p`, if pair_equal(p, slot[j]) returns true, copies `p` to unspecified locations + * in `[probe_output_begin, probe_output_end)` and copies slot[j] to unspecified locations in + * `[contained_output_begin, contained_output_end)`. If `p` does not have any matches, copies + * `p` and a pair of `empty_key_sentinel` and `empty_value_sentinel` into the output. + * + * @tparam buffer_size Size of the output buffer + * @tparam FlushingCG Type of Cooperative Group used to flush output buffer + * @tparam atomicT Type of atomic storage + * @tparam OutputIt1 Device accessible output iterator whose `value_type` is constructible from + * `InputIt`s `value_type`. + * @tparam OutputIt2 Device accessible output iterator whose `value_type` is constructible from + * the map's `value_type`. + * @tparam PairEqual Binary callable type + * @param flushing_cg The Cooperative Group used to flush output buffer + * @param probing_cg The Cooperative Group used to retrieve + * @param pair The pair to search for + * @param flushing_cg_counter Pointer to the flushing CG counter + * @param probe_output_buffer Buffer of the matched probe pair sequence + * @param contained_output_buffer Buffer of the matched contained pair sequence + * @param num_matches Size of the output sequence + * @param probe_output_begin Beginning of the output sequence of the matched probe pairs + * @param contained_output_begin Beginning of the output sequence of the matched contained + * pairs + * @param pair_equal The binary callable used to compare two pairs for equality + */ + template + __device__ __forceinline__ void pair_retrieve_outer( + FlushingCG const& flushing_cg, + cooperative_groups::thread_block_tile const& probing_cg, + value_type const& pair, + uint32_t* flushing_cg_counter, + value_type* probe_output_buffer, + value_type* contained_output_buffer, + atomicT* num_matches, + OutputIt1 probe_output_begin, + OutputIt2 contained_output_begin, + PairEqual pair_equal) noexcept; + + private: + device_view_impl impl_; + }; // class device_view + + /** + * @brief Return the raw pointer of the hash map slots. + */ + value_type* raw_slots() noexcept + { + // Unsafe access to the slots stripping away their atomic-ness to allow non-atomic access. + // TODO: to be replace by atomic_ref when it's ready + return reinterpret_cast(slots_.get()); + } + + /** + * @brief Return the raw pointer of the hash map slots. + */ + value_type const* raw_slots() const noexcept + { + // Unsafe access to the slots stripping away their atomic-ness to allow non-atomic access. + // TODO: to be replace by atomic_ref when it's ready + return reinterpret_cast(slots_.get()); + } + + /** + * @brief Gets the maximum number of elements the hash map can hold. + * + * @return The maximum number of elements the hash map can hold + */ + std::size_t get_capacity() const noexcept { return capacity_; } + + /** + * @brief Gets the number of elements in the hash map. + * + * @param stream CUDA stream used to get the number of inserted elements + * @return The number of elements in the map + */ + std::size_t get_size(cudaStream_t stream = 0) const noexcept; + + /** + * @brief Gets the load factor of the hash map. + * + * @param stream CUDA stream used to get the load factor + * @return The load factor of the hash map + */ + float get_load_factor(cudaStream_t stream = 0) const noexcept; + + /** + * @brief Gets the sentinel value used to represent an empty key slot. + * + * @return The sentinel value used to represent an empty key slot + */ + Key get_empty_key_sentinel() const noexcept { return empty_key_sentinel_; } + + /** + * @brief Gets the sentinel value used to represent an empty value slot. + * + * @return The sentinel value used to represent an empty value slot + */ + Value get_empty_value_sentinel() const noexcept { return empty_value_sentinel_; } + + /** + * @brief Constructs a device_view object based on the members of the `static_multimap` + * object. + * + * @return A device_view object based on the members of the `static_multimap` object + */ + device_view get_device_view() const noexcept + { + return device_view(slots_.get(), capacity_, empty_key_sentinel_, empty_value_sentinel_); + } + + /** + * @brief Constructs a device_mutable_view object based on the members of the + * `static_multimap` object + * + * @return A device_mutable_view object based on the members of the `static_multimap` object + */ + device_mutable_view get_device_mutable_view() const noexcept + { + return device_mutable_view(slots_.get(), capacity_, empty_key_sentinel_, empty_value_sentinel_); + } + + private: + std::size_t capacity_{}; ///< Total number of slots + Key empty_key_sentinel_{}; ///< Key value that represents an empty slot + Value empty_value_sentinel_{}; ///< Initial value of empty slot + slot_allocator_type slot_allocator_{}; ///< Allocator used to allocate slots + counter_allocator_type counter_allocator_{}; ///< Allocator used to allocate counters + cudaStream_t stream_{}; ///< CUDA stream used for ctor/dtor + counter_deleter delete_counter_; ///< Custom counter deleter + slot_deleter delete_slots_; ///< Custom slots deleter + std::unique_ptr d_counter_{}; ///< Preallocated device counter + std::unique_ptr slots_{}; ///< Pointer to flat slots storage +}; // class static_multimap + +} // namespace cuco + +#include +#include diff --git a/include/cuco/traits.hpp b/include/cuco/traits.hpp new file mode 100644 index 000000000..445a40daf --- /dev/null +++ b/include/cuco/traits.hpp @@ -0,0 +1,61 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +namespace cuco { + +/** + * @brief Customization point that can be specialized to indicate that it is safe to perform bitwise + * equality comparisons on the object-representation of objects of type `T`. + * + * By default, only types where `std::has_unique_object_representations_v` is true are safe for + * bitwise equality. However, this can be too restrictive for some types, e.g., floating point + * types. + * + * User-defined specializations of `is_bitwise_comparable` are allowed, but it is the users + * responsibility to ensure values do not occur that would lead to unexpected behavior. For example, + * if a `NaN` bit pattern were used as the empty sentinel value, it may not compare bitwise equal to + * other `NaN` bit patterns. + * + */ +template +struct is_bitwise_comparable : std::false_type { +}; + +/// By default, only types with unique object representations are allowed +template +struct is_bitwise_comparable>> + : std::true_type { +}; + +template +inline constexpr bool is_bitwise_comparable_v = is_bitwise_comparable::value; + +/** + * @brief Declares that a type `Type` is bitwise comparable. + * + */ +#define CUCO_DECLARE_BITWISE_COMPARABLE(Type) \ + namespace cuco { \ + template <> \ + struct is_bitwise_comparable : std::true_type { \ + }; \ + } + +} // namespace cuco diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 40bd2b30a..33bb1bfc4 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -45,3 +45,8 @@ set(DYNAMIC_MAP_TEST_SRC ConfigureTest(DYNAMIC_MAP_TEST "${DYNAMIC_MAP_TEST_SRC}") #################################################################################################### +set(STATIC_MULTIMAP_TEST_SRC + "${CMAKE_CURRENT_SOURCE_DIR}/static_multimap/static_multimap_test.cu") + + ConfigureTest(STATIC_MULTIMAP_TEST "${STATIC_MULTIMAP_TEST_SRC}") +#################################################################################################### diff --git a/tests/static_multimap/static_multimap_test.cu b/tests/static_multimap/static_multimap_test.cu new file mode 100644 index 000000000..8191ab0d9 --- /dev/null +++ b/tests/static_multimap/static_multimap_test.cu @@ -0,0 +1,703 @@ +/* + * Copyright (c) 2021, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include + +#include +#include +#include +#include + +#include + +namespace { +namespace cg = cooperative_groups; + +// Thrust logical algorithms (any_of/all_of/none_of) don't work with device +// lambdas: See https://github.com/thrust/thrust/issues/1062 +template +bool all_of(Iterator begin, Iterator end, Predicate p) +{ + auto size = thrust::distance(begin, end); + return size == thrust::count_if(begin, end, p); +} + +template +bool any_of(Iterator begin, Iterator end, Predicate p) +{ + return thrust::count_if(begin, end, p) > 0; +} + +template +bool none_of(Iterator begin, Iterator end, Predicate p) +{ + return not all_of(begin, end, p); +} + +} // namespace + +enum class probe_sequence { linear_probing, double_hashing }; + +// User-defined key type +// Need to specify alignment to WAR libcu++ bug where cuda::atomic fails for underaligned types: +// https://github.com/NVIDIA/libcudacxx/issues/160 +struct alignas(8) key_pair { + int32_t a; + int32_t b; + __device__ bool operator!=(key_pair const& other) const { return a != other.a and b != other.b; } +}; + +struct hash_key_pair { + __device__ uint32_t operator()(key_pair k) { return k.a; }; +}; + +struct key_pair_equals { + __device__ bool operator()(const key_pair& lhs, const key_pair& rhs) + { + return std::tie(lhs.a, lhs.b) == std::tie(rhs.a, rhs.b); + } +}; + +struct alignas(8) value_pair { + int32_t f; + int32_t s; +}; + +template +__inline__ void test_custom_key_value_type(Map& map, + PairIt pair_begin, + KeyIt key_begin, + size_t num_pairs) +{ + constexpr cudaStream_t stream = 0; + + SECTION("All inserted keys-value pairs should be correctly recovered during find") + { + map.insert(pair_begin, pair_begin + num_pairs); + + auto res = map.get_size(); + REQUIRE(res == num_pairs); + + auto count = map.count(key_begin, key_begin + num_pairs, stream, key_pair_equals{}); + REQUIRE(count == num_pairs); + + thrust::device_vector> found_pairs(num_pairs); + auto output_end = map.retrieve( + key_begin, key_begin + num_pairs, found_pairs.begin(), stream, key_pair_equals{}); + auto size = output_end - found_pairs.begin(); + + REQUIRE(size == num_pairs); + + // sort before compare + thrust::sort( + thrust::device, + found_pairs.begin(), + found_pairs.end(), + [] __device__(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) { return lhs.first.a < rhs.first.a; }); + + REQUIRE(thrust::equal( + thrust::device, + pair_begin, + pair_begin + num_pairs, + found_pairs.begin(), + [] __device__(cuco::pair_type lhs, cuco::pair_type rhs) { + return lhs.first.a == rhs.first.a; + })); + } + + SECTION("Non-matches are not included in the output") + { + map.insert(pair_begin, pair_begin + num_pairs); + + auto const num = num_pairs * 2; + thrust::device_vector query_keys(num); + auto query_key_begin = query_keys.begin(); + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num), + query_key_begin, + [] __device__(auto i) { + return Key{i, i}; + }); + + auto count = map.count(query_key_begin, query_key_begin + num, stream, key_pair_equals{}); + REQUIRE(count == num_pairs); + + thrust::device_vector> found_pairs(num_pairs); + auto output_end = map.retrieve( + query_key_begin, query_key_begin + num, found_pairs.begin(), stream, key_pair_equals{}); + auto size = output_end - found_pairs.begin(); + + REQUIRE(size == num_pairs); + + // sort before compare + thrust::sort( + thrust::device, + found_pairs.begin(), + found_pairs.end(), + [] __device__(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) { return lhs.first.a < rhs.first.a; }); + + REQUIRE(thrust::equal( + thrust::device, + pair_begin, + pair_begin + num_pairs, + found_pairs.begin(), + [] __device__(cuco::pair_type lhs, cuco::pair_type rhs) { + return lhs.first.a == rhs.first.a; + })); + } + + SECTION("Outer functions include non-matches in the output") + { + map.insert(pair_begin, pair_begin + num_pairs); + + auto const num = num_pairs * 2; + thrust::device_vector query_keys(num); + auto query_key_begin = query_keys.begin(); + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num), + query_key_begin, + [] __device__(auto i) { + return Key{i, i}; + }); + + auto count_outer = + map.count_outer(query_key_begin, query_key_begin + num, stream, key_pair_equals{}); + REQUIRE(count_outer == num); + + thrust::device_vector> found_pairs(num); + auto output_end = map.retrieve_outer( + query_key_begin, query_key_begin + num, found_pairs.begin(), stream, key_pair_equals{}); + auto size_outer = output_end - found_pairs.begin(); + + REQUIRE(size_outer == num); + } + + SECTION("All inserted keys-value pairs should be contained") + { + map.insert(pair_begin, pair_begin + num_pairs); + + auto size = map.get_size(); + REQUIRE(size == num_pairs); + + thrust::device_vector contained(num_pairs); + map.contains(key_begin, key_begin + num_pairs, contained.begin(), stream, key_pair_equals{}); + REQUIRE(all_of(contained.begin(), contained.end(), [] __device__(bool const& b) { return b; })); + } + + SECTION("Non-inserted keys-value pairs should not be contained") + { + auto size = map.get_size(); + REQUIRE(size == 0); + + thrust::device_vector contained(num_pairs); + map.contains(key_begin, key_begin + num_pairs, contained.begin(), stream, key_pair_equals{}); + REQUIRE( + none_of(contained.begin(), contained.end(), [] __device__(bool const& b) { return b; })); + } +} + +TEMPLATE_TEST_CASE_SIG("User defined key and value type", + "", + ((probe_sequence Probe), Probe), + (probe_sequence::linear_probing), + (probe_sequence::double_hashing)) +{ + using Key = key_pair; + using Value = value_pair; + + auto constexpr sentinel_key = Key{-1, -1}; + auto constexpr sentinel_value = Value{-1, -1}; + + constexpr std::size_t num_pairs = 100; + constexpr std::size_t capacity = num_pairs * 2; + + thrust::device_vector insert_keys(num_pairs); + thrust::device_vector insert_values(num_pairs); + + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_pairs), + insert_keys.begin(), + [] __device__(auto i) { + return Key{i, i}; + }); + + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_pairs), + insert_values.begin(), + [] __device__(auto i) { + return Value{i, i}; + }); + auto insert_pairs = + thrust::make_zip_iterator(thrust::make_tuple(insert_keys.begin(), insert_values.begin())); + + if constexpr (Probe == probe_sequence::linear_probing) { + cuco::static_multimap, + cuco::linear_probing<1, hash_key_pair>> + map{capacity, sentinel_key, sentinel_value}; + test_custom_key_value_type(map, insert_pairs, insert_keys.begin(), num_pairs); + } + if constexpr (Probe == probe_sequence::double_hashing) { + cuco::static_multimap map{capacity, sentinel_key, sentinel_value}; + test_custom_key_value_type(map, insert_pairs, insert_keys.begin(), num_pairs); + } +} + +template +__inline__ void test_multiplicity_two( + Map& map, PairIt pair_begin, KeyIt key_begin, ResultIt result_begin, std::size_t num_items) +{ + auto num_keys = num_items / 2; + thrust::device_vector d_contained(num_keys); + + SECTION("Non-inserted key/value pairs should not be contained.") + { + auto size = map.get_size(); + REQUIRE(size == 0); + + map.contains(key_begin, key_begin + num_keys, d_contained.begin()); + + REQUIRE( + none_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; })); + } + + map.insert(pair_begin, pair_begin + num_items); + + SECTION("All inserted key/value pairs should be contained.") + { + auto size = map.get_size(); + REQUIRE(size == num_items); + + map.contains(key_begin, key_begin + num_keys, d_contained.begin()); + + REQUIRE( + all_of(d_contained.begin(), d_contained.end(), [] __device__(bool const& b) { return b; })); + } + + SECTION("Total count should be equal to the number of inserted pairs.") + { + // Count matching keys + auto num = map.count(key_begin, key_begin + num_keys); + + REQUIRE(num == num_items); + + auto output_begin = result_begin; + auto output_end = map.retrieve(key_begin, key_begin + num_keys, output_begin); + auto size = thrust::distance(output_begin, output_end); + + REQUIRE(size == num_items); + + // sort before compare + thrust::sort(thrust::device, + output_begin, + output_end, + [] __device__(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) { + if (lhs.first != rhs.first) { return lhs.first < rhs.first; } + return lhs.second < rhs.second; + }); + + REQUIRE(thrust::equal( + thrust::device, + pair_begin, + pair_begin + num_items, + output_begin, + [] __device__(cuco::pair_type lhs, cuco::pair_type rhs) { + return lhs.first == rhs.first and lhs.second == rhs.second; + })); + } + + SECTION("count and count_outer should return the same value.") + { + auto num = map.count(key_begin, key_begin + num_keys); + auto num_outer = map.count_outer(key_begin, key_begin + num_keys); + + REQUIRE(num == num_outer); + } + + SECTION("Output of retrieve and retrieve_outer should be the same.") + { + auto output_begin = result_begin; + auto output_end = map.retrieve(key_begin, key_begin + num_keys, output_begin); + auto size = thrust::distance(output_begin, output_end); + + output_end = map.retrieve_outer(key_begin, key_begin + num_keys, output_begin); + auto size_outer = thrust::distance(output_begin, output_end); + + REQUIRE(size == size_outer); + + // sort before compare + thrust::sort(thrust::device, + output_begin, + output_end, + [] __device__(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) { + if (lhs.first != rhs.first) { return lhs.first < rhs.first; } + return lhs.second < rhs.second; + }); + + REQUIRE(thrust::equal( + thrust::device, + pair_begin, + pair_begin + num_items, + output_begin, + [] __device__(cuco::pair_type lhs, cuco::pair_type rhs) { + return lhs.first == rhs.first and lhs.second == rhs.second; + })); + } +} + +TEMPLATE_TEST_CASE_SIG("Multiplicity equals two", + "", + ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe), + (int32_t, int32_t, probe_sequence::linear_probing), + (int32_t, int64_t, probe_sequence::linear_probing), + (int64_t, int64_t, probe_sequence::linear_probing), + (int32_t, int32_t, probe_sequence::double_hashing), + (int32_t, int64_t, probe_sequence::double_hashing), + (int64_t, int64_t, probe_sequence::double_hashing)) +{ + constexpr std::size_t num_items{4}; + + thrust::device_vector d_keys(num_items / 2); + thrust::device_vector> d_pairs(num_items); + + thrust::sequence(thrust::device, d_keys.begin(), d_keys.end()); + // multiplicity = 2 + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_items), + d_pairs.begin(), + [] __device__(auto i) { + return cuco::pair_type{i / 2, i}; + }); + + thrust::device_vector> d_results(num_items); + + if constexpr (Probe == probe_sequence::linear_probing) { + cuco::static_multimap, + cuco::linear_probing<1, cuco::detail::MurmurHash3_32>> + map{5, -1, -1}; + test_multiplicity_two( + map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items); + } + if constexpr (Probe == probe_sequence::double_hashing) { + cuco::static_multimap map{5, -1, -1}; + test_multiplicity_two( + map, d_pairs.begin(), d_keys.begin(), d_results.begin(), num_items); + } +} + +template +__inline__ void test_non_matches(Map& map, PairIt pair_begin, KeyIt key_begin, std::size_t num_keys) +{ + map.insert(pair_begin, pair_begin + num_keys); + + auto res = map.get_size(); + REQUIRE(res == num_keys); + + SECTION("Output of count and retrieve should be coherent.") + { + auto num = map.count(key_begin, key_begin + num_keys); + thrust::device_vector> d_results(num); + + REQUIRE(num == num_keys); + + auto output_begin = d_results.data().get(); + auto output_end = map.retrieve(key_begin, key_begin + num_keys, output_begin); + auto size = thrust::distance(output_begin, output_end); + + REQUIRE(size == num_keys); + + // sort before compare + thrust::sort(thrust::device, + output_begin, + output_end, + [] __device__(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) { + if (lhs.first != rhs.first) { return lhs.first < rhs.first; } + return lhs.second < rhs.second; + }); + + REQUIRE(thrust::equal( + thrust::device, + pair_begin, + pair_begin + num_keys, + output_begin, + [] __device__(cuco::pair_type lhs, cuco::pair_type rhs) { + return lhs.first == rhs.first and lhs.second == rhs.second; + })); + } + + SECTION("Output of count_outer and retrieve_outer should be coherent.") + { + auto num = map.count_outer(key_begin, key_begin + num_keys); + thrust::device_vector> d_results(num); + + REQUIRE(num == (num_keys + num_keys / 2)); + + auto output_begin = d_results.data().get(); + auto output_end = map.retrieve_outer(key_begin, key_begin + num_keys, output_begin); + auto size = thrust::distance(output_begin, output_end); + + REQUIRE(size == (num_keys + num_keys / 2)); + + // sort before compare + thrust::sort(thrust::device, + output_begin, + output_end, + [] __device__(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) { + if (lhs.first != rhs.first) { return lhs.first < rhs.first; } + return lhs.second < rhs.second; + }); + + // create gold reference + thrust::device_vector> gold(size); + auto gold_begin = gold.begin(); + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(size), + gold_begin, + [num_keys] __device__(auto i) { + if (i < num_keys) { return cuco::pair_type{i / 2, i}; } + return cuco::pair_type{i - num_keys / 2, -1}; + }); + + REQUIRE(thrust::equal( + thrust::device, + gold_begin, + gold_begin + size, + output_begin, + [] __device__(cuco::pair_type lhs, cuco::pair_type rhs) { + return lhs.first == rhs.first and lhs.second == rhs.second; + })); + } +} + +TEMPLATE_TEST_CASE_SIG("Tests of non-matches", + "", + ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe), + (int32_t, int32_t, probe_sequence::linear_probing), + (int32_t, int64_t, probe_sequence::linear_probing), + (int64_t, int64_t, probe_sequence::linear_probing), + (int32_t, int32_t, probe_sequence::double_hashing), + (int32_t, int64_t, probe_sequence::double_hashing), + (int64_t, int64_t, probe_sequence::double_hashing)) +{ + constexpr std::size_t num_keys{1'000'000}; + + thrust::device_vector d_keys(num_keys); + thrust::device_vector> d_pairs(num_keys); + + thrust::sequence(thrust::device, d_keys.begin(), d_keys.end()); + // multiplicity = 2 + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_keys), + d_pairs.begin(), + [] __device__(auto i) { + return cuco::pair_type{i / 2, i}; + }); + + if constexpr (Probe == probe_sequence::linear_probing) { + cuco::static_multimap, + cuco::linear_probing<1, cuco::detail::MurmurHash3_32>> + map{num_keys * 2, -1, -1}; + test_non_matches(map, d_pairs.begin(), d_keys.begin(), num_keys); + } + if constexpr (Probe == probe_sequence::double_hashing) { + cuco::static_multimap map{num_keys * 2, -1, -1}; + test_non_matches(map, d_pairs.begin(), d_keys.begin(), num_keys); + } +} + +template +__inline__ void test_insert_if(Map& map, PairIt pair_begin, KeyIt key_begin, std::size_t size) +{ + // 50% insertion + auto pred_lambda = [] __device__(Key k) { return k % 2 == 0; }; + + map.insert_if(pair_begin, pair_begin + size, key_begin, pred_lambda); + + auto res = map.get_size(); + REQUIRE(res * 2 == size); + + auto num = map.count(key_begin, key_begin + size); + REQUIRE(num * 2 == size); +} + +TEMPLATE_TEST_CASE_SIG("Tests of insert_if", + "", + ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe), + (int32_t, int32_t, probe_sequence::linear_probing), + (int32_t, int64_t, probe_sequence::linear_probing), + (int64_t, int64_t, probe_sequence::linear_probing), + (int32_t, int32_t, probe_sequence::double_hashing), + (int32_t, int64_t, probe_sequence::double_hashing), + (int64_t, int64_t, probe_sequence::double_hashing)) +{ + constexpr std::size_t num_keys{1'000}; + + thrust::device_vector d_keys(num_keys); + thrust::device_vector> d_pairs(num_keys); + + thrust::sequence(thrust::device, d_keys.begin(), d_keys.end()); + // multiplicity = 1 + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_keys), + d_pairs.begin(), + [] __device__(auto i) { + return cuco::pair_type{i, i}; + }); + + if constexpr (Probe == probe_sequence::linear_probing) { + cuco::static_multimap, + cuco::linear_probing<1, cuco::detail::MurmurHash3_32>> + map{num_keys * 2, -1, -1}; + test_insert_if(map, d_pairs.begin(), d_keys.begin(), num_keys); + } + if constexpr (Probe == probe_sequence::double_hashing) { + cuco::static_multimap map{num_keys * 2, -1, -1}; + test_insert_if(map, d_pairs.begin(), d_keys.begin(), num_keys); + } +} + +// Custom pair equal +template +struct pair_equal { + __device__ bool operator()(const cuco::pair_type& lhs, + const cuco::pair_type& rhs) const + { + return lhs.first == rhs.first; + } +}; + +template +__inline__ void test_pair_functions(Map& map, PairIt pair_begin, std::size_t num_pairs) +{ + map.insert(pair_begin, pair_begin + num_pairs); + cudaStreamSynchronize(0); + + auto res = map.get_size(); + REQUIRE(res == num_pairs); + + // query pair matching rate = 50% + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_pairs), + pair_begin, + [] __device__(auto i) { + return cuco::pair_type{i, i}; + }); + + SECTION("Output of pair_count and pair_retrieve should be coherent.") + { + auto num = map.pair_count(pair_begin, pair_begin + num_pairs, pair_equal{}); + + auto out1_begin = thrust::make_zip_iterator( + thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator())); + auto out2_begin = thrust::make_zip_iterator( + thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator())); + + REQUIRE(num == num_pairs); + + auto [out1_end, out2_end] = map.pair_retrieve( + pair_begin, pair_begin + num_pairs, out1_begin, out2_begin, pair_equal{}); + + REQUIRE((out1_end - out1_begin) == num_pairs); + } + + SECTION("Output of pair_count_outer and pair_retrieve_outer should be coherent.") + { + auto num = map.pair_count_outer(pair_begin, pair_begin + num_pairs, pair_equal{}); + + auto out1_begin = thrust::make_zip_iterator( + thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator())); + auto out2_begin = thrust::make_zip_iterator( + thrust::make_tuple(thrust::make_discard_iterator(), thrust::make_discard_iterator())); + + REQUIRE(num == (num_pairs + num_pairs / 2)); + + auto [out1_end, out2_end] = map.pair_retrieve_outer( + pair_begin, pair_begin + num_pairs, out1_begin, out2_begin, pair_equal{}); + + REQUIRE((out1_end - out1_begin) == (num_pairs + num_pairs / 2)); + } +} + +TEMPLATE_TEST_CASE_SIG("Tests of pair functions", + "", + ((typename Key, typename Value, probe_sequence Probe), Key, Value, Probe), + (int32_t, int32_t, probe_sequence::linear_probing), + (int32_t, int64_t, probe_sequence::linear_probing), + (int64_t, int64_t, probe_sequence::linear_probing), + (int32_t, int32_t, probe_sequence::double_hashing), + (int32_t, int64_t, probe_sequence::double_hashing), + (int64_t, int64_t, probe_sequence::double_hashing)) +{ + constexpr std::size_t num_pairs{4}; + thrust::device_vector> d_pairs(num_pairs); + + // pair multiplicity = 2 + thrust::transform(thrust::device, + thrust::counting_iterator(0), + thrust::counting_iterator(num_pairs), + d_pairs.begin(), + [] __device__(auto i) { + return cuco::pair_type{i / 2, i}; + }); + + if constexpr (Probe == probe_sequence::linear_probing) { + cuco::static_multimap, + cuco::linear_probing<1, cuco::detail::MurmurHash3_32>> + map{num_pairs * 2, -1, -1}; + test_pair_functions(map, d_pairs.begin(), num_pairs); + } + if constexpr (Probe == probe_sequence::double_hashing) { + cuco::static_multimap map{num_pairs * 2, -1, -1}; + test_pair_functions(map, d_pairs.begin(), num_pairs); + } +}