Faster struct row comparator

cpp/CMakeLists.txt

@@ -470,6 +470,7 @@ add_library(
   src/structs/structs_column_factories.cu
   src/structs/structs_column_view.cpp
   src/structs/utilities.cpp
+  src/table/row_operators.cu
   src/table/table.cpp
   src/table/table_device_view.cu
   src/table/table_view.cpp

cpp/benchmarks/CMakeLists.txt

@@ -164,6 +164,7 @@ ConfigureBench(SEARCH_BENCH search/search.cpp)
 # ##################################################################################################
 # * sort benchmark --------------------------------------------------------------------------------
 ConfigureBench(SORT_BENCH sort/rank.cpp sort/sort.cpp sort/sort_strings.cpp)
+ConfigureNVBench(SORT_NVBENCH sort/sort_structs.cpp)
 
 # ##################################################################################################
 # * quantiles benchmark

cpp/benchmarks/sort/sort_structs.cpp

@@ -0,0 +1,84 @@
+/*
+ * Copyright (c) 2022, 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 <benchmarks/fixture/rmm_pool_raii.hpp>
+
+#include <cudf/detail/sorting.hpp>
+
+#include <cudf_test/column_utilities.hpp>
+#include <cudf_test/column_wrapper.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+#include <random>
+
+void nvbench_sort_struct(nvbench::state& state)
+{
+  cudf::rmm_pool_raii pool_raii;
+
+  using Type           = int;
+  using column_wrapper = cudf::test::fixed_width_column_wrapper<Type>;
+  std::default_random_engine generator;
+  std::uniform_int_distribution<int> distribution(0, 100);
+
+  const cudf::size_type n_rows{(cudf::size_type)state.get_int64("NumRows")};
+  const cudf::size_type n_cols{1};
+  const cudf::size_type depth{(cudf::size_type)state.get_int64("Depth")};
+  const bool nulls{static_cast<bool>(state.get_int64("Nulls"))};
+
+  // Create columns with values in the range [0,100)
+  std::vector<column_wrapper> columns;
+  columns.reserve(n_cols);
+  std::generate_n(std::back_inserter(columns), n_cols, [&, n_rows]() {
+    auto elements = cudf::detail::make_counting_transform_iterator(
+      0, [&](auto row) { return distribution(generator); });
+    if (!nulls) return column_wrapper(elements, elements + n_rows);
+    auto valids =
+      cudf::detail::make_counting_transform_iterator(0, [](auto i) { return i % 10 != 0; });
+    return column_wrapper(elements, elements + n_rows, valids);
+  });
+
+  std::vector<std::unique_ptr<cudf::column>> cols;
+  std::transform(columns.begin(), columns.end(), std::back_inserter(cols), [](column_wrapper& col) {
+    return col.release();
+  });
+
+  std::vector<std::unique_ptr<cudf::column>> child_cols = std::move(cols);
+  // Lets add some layers
+  for (int i = 0; i < depth; i++) {
+    std::vector<bool> struct_validity;
+    std::uniform_int_distribution<int> bool_distribution(0, 100 * (i + 1));
+    std::generate_n(
+      std::back_inserter(struct_validity), n_rows, [&]() { return bool_distribution(generator); });
+    cudf::test::structs_column_wrapper struct_col(std::move(child_cols), struct_validity);
+    child_cols = std::vector<std::unique_ptr<cudf::column>>{};
+    child_cols.push_back(struct_col.release());
+  }
+
+  // Create table view
+  auto input = cudf::table(std::move(child_cols));
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    rmm::cuda_stream_view stream_view{launch.get_stream()};
+    cudf::detail::sorted_order(input, {}, {}, stream_view, rmm::mr::get_current_device_resource());
+  });
+}
+
+NVBENCH_BENCH(nvbench_sort_struct)
+  .set_name("sort_struct")
+  .add_int64_power_of_two_axis("NumRows", {10, 18, 26})
+  .add_int64_axis("Depth", {1, 8})
+  .add_int64_axis("Nulls", {0, 1});