perf(fastlanes): Evaluate constant list membership

[lwwmanning]

Evaluate constant integer list membership directly against BitPacked arrays. Lists with one to four members use fused comparisons. Larger lists with spans up to 4 KiB use a byte lookup table during FastLanes unpack. Wider spans decode once and reuse the prepared sorted membership plan. A Primitive kernel provides the optimized canonical fallback. Empty lists return false for null needles. Non-empty lists preserve needle validity, and null lists return null.

Local benchmarks cover 64, 1,024, and 65,536 values across 1, 4, 8, 32, and 64 list members. Compressed execution was 21–54% faster than canonical fallback. Tables near the 4 KiB cap were 20–35% faster. Wide sparse sets were 10–16% faster at 1,024 rows and stayed within 1% at 65,536 rows.

🤖 Generated with Codex

[codspeed-hq]

Merging this PR will degrade performance by 5.63%

⚠️ Unknown Walltime execution environment detected

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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

⚡ 7 improved benchmarks
❌ 17 regressed benchmarks
✅ 2145 untouched benchmarks
🆕 50 new benchmarks
⏩ 106 skipped benchmarks¹
🗄️ 4 archived benchmarks run²

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	WallTime	arrow_checked_add_u32_neon[16384]	13.5 µs	20.3 µs	-33.75%
❌	WallTime	mul_u64_nonnull_neon	15.1 µs	20 µs	-24.62%
❌	WallTime	mul_i64_nonnull_neon	17.1 µs	20 µs	-14.76%
❌	WallTime	multiply_shapes_neon[(16384, PerRowPerRow)]	17.3 µs	20.2 µs	-14.32%
❌	Simulation	filter_powerlaw_by_mostly_true[250000]	186.5 µs	215.9 µs	-13.6%
❌	Simulation	filter_random_by_mostly_true[250000]	186.6 µs	215.9 µs	-13.57%
❌	Simulation	compress_fsst[(500, 64, 8)]	475.1 µs	541.5 µs	-12.26%
❌	Simulation	filter_powerlaw_by_random[250000]	170.9 µs	194 µs	-11.91%
❌	Simulation	filter_random_by_random[250000]	170.8 µs	193.9 µs	-11.89%
❌	Simulation	density_sweep_random[0.9]	92.5 µs	104 µs	-11.11%
❌	Simulation	filter_powerlaw_by_mostly_true[100000]	93.6 µs	105.2 µs	-10.99%
❌	Simulation	filter_random_by_mostly_true[100000]	93.2 µs	104.7 µs	-10.97%
❌	Simulation	density_sweep_random[0.95]	93.1 µs	104.5 µs	-10.92%
❌	Simulation	density_sweep_dense_runs[0.9]	92.5 µs	103.8 µs	-10.88%
❌	Simulation	density_sweep_dense_runs[0.95]	93.3 µs	104.5 µs	-10.77%
❌	Simulation	filter_powerlaw_by_power_law[250000]	161.4 µs	179.7 µs	-10.19%
❌	Simulation	filter_random_by_power_law[250000]	161.2 µs	179.4 µs	-10.18%
⚡	Simulation	compact_sliced[(2048, 10)]	251.4 µs	180.9 µs	+38.96%
⚡	Simulation	compact_sliced[(1024, 10)]	149.7 µs	114.3 µs	+30.9%
⚡	WallTime	arrow_checked_add_u32_avx512[16384]	21.3 µs	17.7 µs	+20.4%
...	...	...	...	...	...

ℹ️ Only the first 20 benchmarks are displayed. Go to the app to view all benchmarks.

Tip

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

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_{Comparing wm/fastlanes-list-contains (4c49d32) with develop (01f147a)}

Footnotes

1. 106 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. ↩

2. 4 benchmarks were run, but are now archived. If they were deleted in another branch, consider rebasing to remove them from the report. Instead if they were added back, click here to restore them. ↩