gf2121 commented on PR #14176:
URL: https://github.com/apache/lucene/pull/14176#issuecomment-2639090793
Thanks @iverase !
For the vectorized decodeing, I benchmarked the decoding method with jmh,
the result on my M2 mac:
```
Benchmark Mode Cnt Score Error Units
BKDCodecBenchmark.readInts16ForUtil thrpt 5 94.529 ± 2.886 ops/ms
BKDCodecBenchmark.readInts16Vector thrpt 5 194.320 ± 7.082 ops/ms
BKDCodecBenchmark.readInts24ForUtil thrpt 5 93.435 ± 5.063 ops/ms
BKDCodecBenchmark.readInts24Legacy thrpt 5 81.779 ± 1.390 ops/ms
BKDCodecBenchmark.readInts24Vector thrpt 5 151.203 ± 0.460 ops/ms
```
It suggests that `readInts24ForUtil` and `readInts24Legacy` do not have to
much difference, which is consistent with previous luceneutil result:
> The previous result was got by taskRepeatCount=20 . I find that the
speedup disappeared when taskRepeatCount increased to 50:
TaskQPS baseline StdDevQPS
my_modified_version StdDev Pct diff p-value
TermDayOfYearSort 196.21 (8.7%) 194.85
(11.2%) -0.7% ( -18% - 21%) 0.871
CountFilteredIntNRQ 84.92 (13.1%) 84.84
(12.1%) -0.1% ( -22% - 28%) 0.987
IntNRQ 137.14 (20.2%) 137.30
(18.4%) 0.1% ( -31% - 48%) 0.989
FilteredIntNRQ 134.41 (20.0%) 135.05
(18.1%) 0.5% ( -31% - 48%) 0.954
TermDTSort 196.18 (9.0%) 201.19
(9.0%) 2.6% ( -14% - 22%) 0.506
The vectorized decoding method using vector API seems perform much better,
I'll try to run luceneutil to confirm end-to-end result. I'll keep this PR
simple and leave vectorized decoding optimization to another PR.
https://github.com/apache/lucene/pull/14203
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