tang-hi commented on PR #12417:
URL: https://github.com/apache/lucene/pull/12417#issuecomment-1631024688
Hi, everyone. I tried the lazy compute idea that I mentioned before. First,
I attempted to change the code in the main branch to lazy compute, the
benchmark results didn't show much difference. Then I applied the lazy compute
algorithm to the vectorized code, and the benchmark results showed improved
performance. However, I was surprised to see that the benchmark results for
Prefix3 were not good. After that, I tested the scalar code, and the benchmark
results showed a decrease in performance. I have the results of these two
benchemark below.
## vectorized
TaskQPS baseline StdDevQPS
my_modified_version StdDev Pct diff p-value
Prefix3 90.71 (2.4%) 80.20
(4.7%) -11.6% ( -18% - -4%) 0.000
BrowseDayOfYearSSDVFacets 6.89 (13.1%) 6.65
(10.2%) -3.5% ( -23% - 22%) 0.348
Wildcard 56.40 (3.0%) 54.70
(2.9%) -3.0% ( -8% - 3%) 0.001
BrowseDateTaxoFacets 5.72 (11.1%) 5.58
(7.9%) -2.5% ( -19% - 18%) 0.415
BrowseRandomLabelTaxoFacets 5.54 (8.6%) 5.42
(7.4%) -2.3% ( -16% - 14%) 0.369
BrowseDayOfYearTaxoFacets 5.73 (10.3%) 5.61
(7.5%) -2.1% ( -18% - 17%) 0.454
BrowseMonthSSDVFacets 7.01 (11.3%) 6.86
(11.9%) -2.1% ( -22% - 23%) 0.567
IntNRQ 42.16 (4.9%) 41.72
(2.7%) -1.1% ( -8% - 6%) 0.397
Fuzzy2 81.88 (1.8%) 82.32
(2.3%) 0.5% ( -3% - 4%) 0.411
MedIntervalsOrdered 10.00 (5.2%) 10.05
(4.0%) 0.5% ( -8% - 10%) 0.711
Fuzzy1 114.12 (1.8%) 114.78
(2.4%) 0.6% ( -3% - 4%) 0.385
BrowseRandomLabelSSDVFacets 5.31 (5.5%) 5.35
(8.0%) 0.7% ( -12% - 14%) 0.746
TermDTSort 159.43 (5.3%) 160.63
(4.0%) 0.8% ( -8% - 10%) 0.612
MedTermDayTaxoFacets 11.07 (3.0%) 11.16
(2.8%) 0.8% ( -4% - 6%) 0.381
HighTermTitleSort 100.01 (3.8%) 101.02
(4.6%) 1.0% ( -7% - 9%) 0.447
MedSpanNear 45.12 (2.5%) 45.61
(2.3%) 1.1% ( -3% - 6%) 0.157
HighIntervalsOrdered 14.06 (4.6%) 14.22
(4.3%) 1.2% ( -7% - 10%) 0.410
HighTermTitleBDVSort 12.39 (1.7%) 12.54
(2.9%) 1.2% ( -3% - 5%) 0.104
HighSpanNear 17.67 (2.2%) 17.90
(2.0%) 1.3% ( -2% - 5%) 0.047
Respell 68.69 (1.5%) 69.62
(1.5%) 1.4% ( -1% - 4%) 0.005
BrowseDateSSDVFacets 1.78 (13.0%) 1.81
(11.8%) 1.6% ( -20% - 30%) 0.676
AndHighHighDayTaxoFacets 3.79 (3.4%) 3.86
(3.4%) 2.0% ( -4% - 9%) 0.067
AndHighMedDayTaxoFacets 21.71 (2.5%) 22.16
(2.4%) 2.1% ( -2% - 7%) 0.007
OrHighMedDayTaxoFacets 8.33 (5.1%) 8.52
(3.5%) 2.3% ( -6% - 11%) 0.103
PKLookup 266.99 (2.7%) 273.32
(2.4%) 2.4% ( -2% - 7%) 0.003
OrHighNotLow 536.68 (3.5%) 551.31
(4.7%) 2.7% ( -5% - 11%) 0.037
HighTerm 772.12 (2.2%) 795.02
(3.7%) 3.0% ( -2% - 9%) 0.002
LowTerm 777.24 (2.1%) 803.48
(4.0%) 3.4% ( -2% - 9%) 0.001
MedTerm 625.99 (3.1%) 647.55
(5.4%) 3.4% ( -4% - 12%) 0.013
MedSloppyPhrase 18.33 (1.8%) 18.98
(2.8%) 3.5% ( 0% - 8%) 0.000
OrHighNotMed 461.93 (3.6%) 478.49
(4.2%) 3.6% ( -4% - 11%) 0.004
OrHighNotHigh 526.32 (2.9%) 546.51
(3.7%) 3.8% ( -2% - 10%) 0.000
OrNotHighMed 417.97 (2.6%) 434.74
(3.1%) 4.0% ( -1% - 9%) 0.000
OrNotHighHigh 514.95 (2.9%) 535.68
(2.7%) 4.0% ( -1% - 9%) 0.000
OrHighHigh 37.60 (3.5%) 39.18
(4.5%) 4.2% ( -3% - 12%) 0.001
HighSloppyPhrase 2.28 (2.3%) 2.38
(2.4%) 4.3% ( 0% - 9%) 0.000
AndHighHigh 38.76 (2.1%) 40.63
(2.8%) 4.8% ( 0% - 9%) 0.000
HighTermDayOfYearSort 343.83 (2.4%) 360.72
(4.7%) 4.9% ( -2% - 12%) 0.000
LowSloppyPhrase 60.08 (1.6%) 63.04
(2.2%) 4.9% ( 1% - 8%) 0.000
OrNotHighLow 647.85 (1.7%) 680.90
(2.5%) 5.1% ( 0% - 9%) 0.000
LowIntervalsOrdered 5.71 (3.6%) 6.00
(2.8%) 5.1% ( -1% - 11%) 0.000
HighPhrase 28.70 (1.7%) 30.18
(1.7%) 5.2% ( 1% - 8%) 0.000
LowSpanNear 7.41 (2.1%) 7.80
(1.9%) 5.3% ( 1% - 9%) 0.000
MedPhrase 11.08 (1.5%) 11.71
(2.1%) 5.8% ( 2% - 9%) 0.000
OrHighLow 520.52 (1.6%) 552.94
(3.5%) 6.2% ( 1% - 11%) 0.000
AndHighLow 1225.95 (2.7%) 1308.18
(5.8%) 6.7% ( -1% - 15%) 0.000
HighTermMonthSort 3123.59 (2.9%) 3381.95
(5.3%) 8.3% ( 0% - 16%) 0.000
LowPhrase 27.73 (1.3%) 30.05
(2.2%) 8.4% ( 4% - 12%) 0.000
AndHighMed 109.92 (1.8%) 119.78
(2.7%) 9.0% ( 4% - 13%) 0.000
OrHighMed 111.14 (2.2%) 121.78
(3.3%) 9.6% ( 3% - 15%) 0.000
BrowseMonthTaxoFacets 16.77 (28.5%) 18.90
(1.4%) 12.7% ( -13% - 59%) 0.046
## scalar
TaskQPS baseline StdDevQPS
my_modified_version StdDev Pct diff p-value
LowSloppyPhrase 9.77 (4.4%) 5.73
(2.1%) -41.4% ( -45% - -36%) 0.000
MedSpanNear 11.23 (4.6%) 6.95
(2.3%) -38.2% ( -43% - -32%) 0.000
AndHighMed 123.37 (8.0%) 76.65
(2.0%) -37.9% ( -44% - -30%) 0.000
AndHighMedDayTaxoFacets 26.15 (2.0%) 16.34
(1.6%) -37.5% ( -40% - -34%) 0.000
HighSpanNear 13.97 (3.2%) 9.09
(1.8%) -34.9% ( -38% - -30%) 0.000
MedIntervalsOrdered 21.95 (3.5%) 14.81
(2.7%) -32.5% ( -37% - -27%) 0.000
AndHighLow 1070.91 (3.9%) 732.57
(2.7%) -31.6% ( -36% - -25%) 0.000
LowPhrase 20.41 (2.9%) 14.15
(2.3%) -30.7% ( -34% - -26%) 0.000
MedSloppyPhrase 28.12 (2.8%) 19.93
(1.2%) -29.1% ( -32% - -25%) 0.000
LowIntervalsOrdered 3.36 (4.6%) 2.39
(3.3%) -28.8% ( -35% - -21%) 0.000
OrNotHighLow 640.26 (2.8%) 488.06
(1.8%) -23.8% ( -27% - -19%) 0.000
AndHighHighDayTaxoFacets 10.74 (2.1%) 8.19
(2.1%) -23.7% ( -27% - -19%) 0.000
LowSpanNear 277.31 (1.8%) 212.48
(1.9%) -23.4% ( -26% - -20%) 0.000
AndHighHigh 25.31 (5.6%) 19.62
(2.7%) -22.5% ( -29% - -15%) 0.000
OrNotHighMed 528.99 (2.2%) 414.93
(2.7%) -21.6% ( -25% - -17%) 0.000
OrHighLow 408.04 (3.1%) 322.73
(2.7%) -20.9% ( -25% - -15%) 0.000
HighSloppyPhrase 13.08 (3.5%) 10.65
(2.0%) -18.6% ( -23% - -13%) 0.000
OrHighHigh 23.91 (5.3%) 19.49
(2.5%) -18.5% ( -24% - -11%) 0.000
MedPhrase 187.62 (2.3%) 153.52
(1.7%) -18.2% ( -21% - -14%) 0.000
OrHighMed 31.30 (4.8%) 26.68
(3.4%) -14.8% ( -21% - -6%) 0.000
HighIntervalsOrdered 0.74 (5.2%) 0.64
(4.7%) -14.1% ( -22% - -4%) 0.000
HighTermDayOfYearSort 327.16 (2.4%) 288.14
(3.1%) -11.9% ( -16% - -6%) 0.000
HighTermTitleSort 105.28 (6.8%) 93.57
(5.1%) -11.1% ( -21% - 0%) 0.000
HighPhrase 173.78 (2.5%) 156.10
(1.4%) -10.2% ( -13% - -6%) 0.000
TermDTSort 166.57 (5.5%) 151.19
(4.4%) -9.2% ( -18% - 0%) 0.000
MedTerm 641.21 (3.4%) 583.88
(3.2%) -8.9% ( -15% - -2%) 0.000
OrNotHighHigh 533.02 (2.8%) 486.18
(2.0%) -8.8% ( -13% - -4%) 0.000
OrHighNotLow 516.66 (2.9%) 472.41
(4.7%) -8.6% ( -15% - 0%) 0.000
OrHighMedDayTaxoFacets 8.58 (3.7%) 7.85
(3.8%) -8.4% ( -15% - 0%) 0.000
OrHighNotMed 496.57 (3.1%) 457.70
(2.7%) -7.8% ( -13% - -2%) 0.000
HighTerm 587.86 (4.2%) 542.76
(3.0%) -7.7% ( -14% - 0%) 0.000
OrHighNotHigh 770.22 (2.5%) 723.29
(2.9%) -6.1% ( -11% - 0%) 0.000
Fuzzy2 31.75 (2.2%) 29.98
(5.6%) -5.6% ( -13% - 2%) 0.000
MedTermDayTaxoFacets 38.41 (1.8%) 36.45
(1.4%) -5.1% ( -8% - -1%) 0.000
LowTerm 900.74 (2.3%) 866.32
(3.2%) -3.8% ( -9% - 1%) 0.000
HighTermTitleBDVSort 7.78 (2.9%) 7.61
(3.6%) -2.2% ( -8% - 4%) 0.034
BrowseMonthSSDVFacets 9.22 (6.5%) 9.08
(7.8%) -1.4% ( -14% - 13%) 0.532
PKLookup 260.37 (3.8%) 258.96
(3.6%) -0.5% ( -7% - 7%) 0.645
Respell 54.02 (2.2%) 53.76
(2.2%) -0.5% ( -4% - 4%) 0.492
Fuzzy1 81.60 (2.4%) 81.29
(1.7%) -0.4% ( -4% - 3%) 0.557
BrowseRandomLabelSSDVFacets 6.07 (8.1%) 6.05
(8.3%) -0.3% ( -15% - 17%) 0.894
BrowseDayOfYearSSDVFacets 8.14 (6.9%) 8.12
(7.3%) -0.3% ( -13% - 14%) 0.885
HighTermMonthSort 2956.82 (4.0%) 2967.31
(4.3%) 0.4% ( -7% - 9%) 0.787
Wildcard 73.82 (3.0%) 75.25
(3.5%) 1.9% ( -4% - 8%) 0.061
Prefix3 190.39 (4.0%) 195.04
(3.9%) 2.4% ( -5% - 10%) 0.050
BrowseRandomLabelTaxoFacets 5.77 (11.3%) 5.92
(10.1%) 2.6% ( -16% - 27%) 0.441
BrowseDateTaxoFacets 6.26 (15.9%) 6.43
(15.7%) 2.7% ( -24% - 40%) 0.588
BrowseDayOfYearTaxoFacets 6.28 (15.9%) 6.47
(16.0%) 2.9% ( -25% - 41%) 0.565
BrowseMonthTaxoFacets 10.24 (52.4%) 10.54
(54.2%) 2.9% ( -68% - 230%) 0.862
IntNRQ 43.93 (15.3%) 45.33
(8.8%) 3.2% ( -18% - 32%) 0.419
BrowseDateSSDVFacets 1.57 (11.2%) 1.64
(13.6%) 4.6% ( -18% - 33%) 0.244
My current conclusions are:
1. Lazy compute works well when combined with vectors.
2. Perhaps we can improve the performance of the scalar code. The current
version may still have room for improvement, but I currently don't have any
ideas. If you have any suggestions, please feel free to propose them or
directly commit to this branch.
3. The benchmark results for Prefix3 seem odd because it only shows a
performance decrease in the vectorized version, even in the scalar version, the
performance of Prefix3 doesn't decrease. Any clues?
I would love to hear your opinions and welcome any ideas.
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