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Optimize list_contains with prepared constant sets - #10052
12 benchmarks regressed
⚠️ 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.
⚠️ 2 benchmarks measured no execution time
Nothing ran under measurement, usually because the compiler removed the code under test. These results are not comparable, so they count as unchanged.
⚠️ 8 benchmarks spent significant time in system calls
System calls cannot be consistently instrumented, so they are not included in the measure, which understates the real cost. Please switch to the Walltime instrument to accurately measure system calls.
⚠️ Different runtime environments detected
Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.
⚡ 7 improved benchmarks
❌ 12 regressed benchmarks
✅ 2200 untouched benchmarks
Warning
Please fix the performance issues or acknowledge them on CodSpeed.
Performance Changes
| Mode | Benchmark | BASE |
HEAD |
Efficiency | |
|---|---|---|---|---|---|
| ❌ | Simulation | optimize_lookup_predicate[ids=192, shape=in_list] |
51 µs | 76.7 µs | -33.44% |
| ❌ | Simulation | optimize_lookup_predicate[ids=128, shape=in_list] |
51.6 µs | 74 µs | -30.21% |
| ❌ | Simulation | optimize_lookup_predicate[ids=64, shape=in_list] |
51.5 µs | 71.4 µs | -27.83% |
| ❌ | Simulation | optimize_lookup_predicate[ids=16, shape=in_list] |
52.4 µs | 70.2 µs | -25.41% |
| ❌ | Simulation | optimize_lookup_predicate[ids=256, shape=in_list] |
79.6 µs | 106.1 µs | -24.97% |
| ❌ | Simulation | optimize_lookup_predicate[ids=1, shape=in_list] |
75.3 µs | 90.7 µs | -17.07% |
| ❌ | Simulation | sum_v2_i64 |
194.2 µs | 224.9 µs | -13.63% |
| ❌ | Simulation | sum_i64 |
194.4 µs | 224.7 µs | -13.48% |
| ❌ | Simulation | random_i8[0.01] |
108.4 µs | 123.9 µs | -12.5% |
| ❌ | WallTime | filter_avx2[64] |
2.1 µs | 2.4 µs | -12.23% |
| ❌ | WallTime | filter_avx2[80] |
2.2 µs | 2.5 µs | -10.93% |
| ❌ |
Simulation | random_i128[0.01] |
7.2 µs | 8 µs | -10.53% |
| ⚡ | Simulation | i64_random_chunked[256] |
13,907.4 µs | 226.4 µs | ×61 |
| ⚡ | Simulation | utf8_random[256] |
12,973.6 µs | 416.1 µs | ×31 |
| ⚡ | Simulation | i64_random[256] |
3,928.8 µs | 170.1 µs | ×23 |
| ⚡ | Simulation | nested_list_random[32] |
6.7 ms | 1 ms | ×6.4 |
| ⚡ | WallTime | filter_avx512[80] |
3.2 µs | 2.3 µs | +38.85% |
| ⚡ | WallTime | filter_avx512[64] |
2.9 µs | 2.2 µs | +33.18% |
| ⚡ | WallTime | words_gather_dispatch_neon[65536] |
2.3 µs | 2 µs | +10.42% |
Tip
Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.
Comparing claude/friendly-knuth-dj6ed1-list-contains (4bb2b50) with develop (751e0af)
