Add benchmarks for reshaping operations #21212
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Matt711
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This commit adds 50 benchmark test cases for DataFrame reshaping operations (pivot, unstack) to establish baseline performance metrics and identify optimization opportunities. Key features: - 50 total benchmarks covering pivot() and unstack() operations - Parametrized fixtures for clean, DRY design - Tests single-column and multi-column operations (1, 2, 4 value columns) - Data sizes: 1M-10M rows, cardinality: 100-2000 - Supports both cuDF and pandas via CUDF_BENCHMARKS_USE_PANDAS=1 Key findings from benchmarks: - Multi-column pivot: pandas 2.4-3.3x faster (low-medium cardinality) - High cardinality: cuDF 3-25x faster (10M rows, card>1000) - Multi-level unstack: cuDF's best case (25-134x faster) These benchmarks establish a baseline for tracking performance and provide evidence for future optimization work targeting the multi-column performance gap. Co-authored-by: Claude Sonnet 4.5 <[email protected]>
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DetailsGPU: Tesla V100 |
mroeschke
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Jan 28, 2026
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| (1_000_000, 100), | ||
| (1_000_000, 500), | ||
| (1_000_000, 1000), | ||
| (10_000_000, 500), | ||
| (10_000_000, 2000), |
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Could we use NUM_ROWS from config? Ideally we want these benchmarks to run quick when running them locally (and CI) for correctness.
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This commit adds 50 benchmark test cases for DataFrame reshaping operations (pivot, unstack) to establish baseline performance metrics and identify optimization opportunities.
Key features:
Key findings from benchmarks:
Description
Contributes to #20469
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