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[REVIEW] Add notes to performance comparisons notebook #13044

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merged 8 commits into from
Mar 31, 2023
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Merged

[REVIEW] Add notes to performance comparisons notebook #13044

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galipremsagar Mar 30, 2023
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more updates
galipremsagar Mar 30, 2023
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move
galipremsagar Mar 31, 2023
9baf74f
update symlink
galipremsagar Mar 31, 2023
ac91c7c
Update docs/cudf/source/user_guide/performance_comparisons/performanc…
galipremsagar Mar 31, 2023
45e04c0
Merge branch 'branch-23.06' into update_notes_perf
galipremsagar Mar 31, 2023
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update typo
galipremsagar Mar 31, 2023
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Merge branch 'update_notes_perf' of https://github.com/galipremsagar/…
galipremsagar Mar 31, 2023
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16 changes: 10 additions & 6 deletions docs/cudf/source/user_guide/performance_comparisons.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,13 +8,16 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook compares the performance of `cuDF` and `pandas`. The comparisons performed are on identical data sizes. This notebook primarily showcases the factor\n",
"of speedups users can have when the similar `pandas` APIs are run on GPUs using `cudf`.\n",
"\n",
"The hardware details used to run these performance comparisons are at the end of this page."
"The hardware details used to run these performance comparisons are at the end of this page.\n",
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@galipremsagar galipremsagar Mar 30, 2023 •
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I've verified this claim holds true on a T4 by running the entire notebook after slashing the data-sizes in half.

"\n",
"**Note**: This notebook is written to measure performance on modern NVIDIA hardware, for older NVIDIA hardware with lower GPU memory please consider lowering the `num_rows` values below by a factor of 2. Results may vary by data sizes, CPU & GPU used."
]
},
{
Expand Down Expand Up @@ -576,9 +579,10 @@
},
"outputs": [],
"source": [
"num_rows = 300_000_000\n",
"pd_series = pd.Series(\n",
" np.random.choice(\n",
" [\"123\", \"56.234\", \"Walmart\", \"Costco\", \"rapids ai\"], size=300_000_000\n",
" [\"123\", \"56.234\", \"Walmart\", \"Costco\", \"rapids ai\"], size=num_rows\n",
" )\n",
")"
]
Expand Down Expand Up @@ -1368,10 +1372,10 @@
},
"outputs": [],
"source": [
"size = 100_000_000\n",
"num_rows = 100_000_000\n",
"pdf = pd.DataFrame()\n",
"pdf[\"key\"] = np.random.randint(0, 2, size)\n",
"pdf[\"val\"] = np.random.randint(0, 7, size)\n",
"pdf[\"key\"] = np.random.randint(0, 2, num_rows)\n",
"pdf[\"val\"] = np.random.randint(0, 7, num_rows)\n",
"\n",
"\n",
"def custom_formula_udf(df):\n",
Expand Down Expand Up @@ -1634,7 +1638,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.10.10"
},
"vscode": {
"interpreter": {
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