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bench: track small u8 dictionary take - #9566

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bench: track small u8 dictionary take#9566
joseph-isaacs wants to merge 1 commit into
developfrom
ji/small-u8-table-take-benchmark

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@joseph-isaacs

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Rationale

Establish a CodSpeed baseline for the low-cardinality dictionary case before adding the SIMD table kernel.

Changes

  • Add a deterministic StatPopGen GT-shaped dictionary with four entries: 0u8, 1u8, 2u8, and fused null.
  • Use u8 dictionary codes at the observed GT frequencies.
  • Measure 1M and 16M rows.
  • Tag the case with #[vortex_bench_support::cpu_features], routing it to the AVX2, AVX-512, and NEON wall-time legs instead of simulation.

This is the lower PR in a two-PR stack. The SIMD implementation is intentionally absent so this PR records the baseline.

Checks

  • cargo check --locked -p vortex-array --bench take_primitive in simulation routing
  • cargo check --locked -p vortex-array --bench take_primitive with the NEON feature leg
  • targeted Clippy with warnings denied
  • cargo +nightly fmt --all --check
  • git diff --check

Add a StatPopGen GT-shaped four-value dictionary canonicalization benchmark. Route it through the CPU-feature walltime legs for AVX2, AVX-512, and NEON.

Signed-off-by: Joseph Isaacs <joseph-isaacs@users.noreply.github.com>
@codspeed-hq

codspeed-hq Bot commented Aug 24, 2026

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Merging this PR will degrade performance by 12.04%

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

❌ 2 regressed benchmarks
✅ 1979 untouched benchmarks
🆕 6 new benchmarks
⏩ 54 skipped benchmarks1

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

Mode Benchmark BASE HEAD Efficiency
WallTime words_gather_scalar_avx2[65536] 8.3 µs 9.4 µs -12.48%
Simulation cold_misaligned[(16, 64)] 380.2 µs 430.1 µs -11.59%
🆕 WallTime dict_canonicalize_gt_u8_neon[1000000] N/A 546.5 µs N/A
🆕 WallTime dict_canonicalize_gt_u8_neon[16000000] N/A 8.7 ms N/A
🆕 WallTime dict_canonicalize_gt_u8_avx512[1000000] N/A 419.7 µs N/A
🆕 WallTime dict_canonicalize_gt_u8_avx512[16000000] N/A 6.8 ms N/A
🆕 WallTime dict_canonicalize_gt_u8_avx2[1000000] N/A 420.4 µs N/A
🆕 WallTime dict_canonicalize_gt_u8_avx2[16000000] N/A 6.8 ms N/A

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.


Comparing ji/small-u8-table-take-benchmark (d49393c) with develop (ef43456)

Open in CodSpeed

Footnotes

  1. 54 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports.

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