gf2121 opened a new pull request #2139:
URL: https://github.com/apache/lucene-solr/pull/2139
# Description
In `decode6()` `decode7()` `decode14()` `decode15()` `decode24`, longs
always `&` a same mask and do some shift. By printing assemble language, i find
that JIT did not optimize them with SIMD instructions. But when we extract all
`&` operations and do them first, JIT will use SIMD to optimize them.
# Tests
Java Version:
> java version "11.0.6" 2020-01-14 LTS
> Java(TM) SE Runtime Environment 18.9 (build 11.0.6+8-LTS)
> Java HotSpot(TM) 64-Bit Server VM 18.9 (build 11.0.6+8-LTS, mixed mode)
Using `decode15` as an example, here is a microbenchmark based on JMH:
**code**
```
@Benchmark
@BenchmarkMode({Mode.Throughput})
@Fork(1)
@Measurement(iterations = 10, time = 1, timeUnit = TimeUnit.SECONDS)
@Warmup(iterations = 3, time = 1, timeUnit = TimeUnit.SECONDS)
public void decode15a() {
for (int iter = 0, tmpIdx = 0, longsIdx = 30; iter < 2; ++iter,
tmpIdx += 15, longsIdx += 1) {
long l0 = (TMP[tmpIdx+0] & MASK16_1) << 14;
l0 |= (TMP[tmpIdx+1] & MASK16_1) << 13;
l0 |= (TMP[tmpIdx+2] & MASK16_1) << 12;
l0 |= (TMP[tmpIdx+3] & MASK16_1) << 11;
l0 |= (TMP[tmpIdx+4] & MASK16_1) << 10;
l0 |= (TMP[tmpIdx+5] & MASK16_1) << 9;
l0 |= (TMP[tmpIdx+6] & MASK16_1) << 8;
l0 |= (TMP[tmpIdx+7] & MASK16_1) << 7;
l0 |= (TMP[tmpIdx+8] & MASK16_1) << 6;
l0 |= (TMP[tmpIdx+9] & MASK16_1) << 5;
l0 |= (TMP[tmpIdx+10] & MASK16_1) << 4;
l0 |= (TMP[tmpIdx+11] & MASK16_1) << 3;
l0 |= (TMP[tmpIdx+12] & MASK16_1) << 2;
l0 |= (TMP[tmpIdx+13] & MASK16_1) << 1;
l0 |= (TMP[tmpIdx+14] & MASK16_1) << 0;
ARR[longsIdx+0] = l0;
}
}
@Benchmark
@BenchmarkMode({Mode.Throughput})
@Fork(1)
@Measurement(iterations = 10, time = 1, timeUnit = TimeUnit.SECONDS)
@Warmup(iterations = 3, time = 1, timeUnit = TimeUnit.SECONDS)
public void decode15b() {
shiftLongs(TMP, 30, TMP, 0, 0, MASK16_1);
for (int iter = 0, tmpIdx = 0, longsIdx = 30; iter < 2; ++iter,
tmpIdx += 15, longsIdx += 1) {
long l0 = TMP[tmpIdx+0] << 14;
l0 |= TMP[tmpIdx+1] << 13;
l0 |= TMP[tmpIdx+2] << 12;
l0 |= TMP[tmpIdx+3] << 11;
l0 |= TMP[tmpIdx+4] << 10;
l0 |= TMP[tmpIdx+5] << 9;
l0 |= TMP[tmpIdx+6] << 8;
l0 |= TMP[tmpIdx+7] << 7;
l0 |= TMP[tmpIdx+8] << 6;
l0 |= TMP[tmpIdx+9] << 5;
l0 |= TMP[tmpIdx+10] << 4;
l0 |= TMP[tmpIdx+11] << 3;
l0 |= TMP[tmpIdx+12] << 2;
l0 |= TMP[tmpIdx+13] << 1;
l0 |= TMP[tmpIdx+14] << 0;
ARR[longsIdx+0] = l0;
}
}
```
**Result**
```
Benchmark Mode Cnt Score Error Units
MyBenchmark.decode15a thrpt 10 65234108.600 ± 1336311.970 ops/s
MyBenchmark.decode15b thrpt 10 106840656.363 ± 448026.092 ops/s
```
And an end-to-end test based on _wikimedium1m_ also looks positive overall:
```
Fuzzy1 131.77 (5.4%) 131.75 (4.2%) -0.0% ( -9% -
10%) 0.990
MedPhrase 146.41 (4.5%) 146.44 (4.8%)
0.0% ( -8% - 9%) 0.992
AndHighMed 643.10 (5.4%) 643.95 (5.5%)
0.1% ( -10% - 11%) 0.939
HighSpanNear 125.99 (5.7%) 126.48 (4.9%)
0.4% ( -9% - 11%) 0.818
Respell 164.81 (4.9%) 165.48 (4.5%)
0.4% ( -8% - 10%) 0.783
HighSloppyPhrase 103.20 (6.2%) 103.65 (5.8%)
0.4% ( -10% - 13%) 0.816
IntNRQ 662.80 (5.0%) 665.87 (5.1%)
0.5% ( -9% - 11%) 0.770
Prefix3 882.57 (6.8%) 887.18 (8.6%)
0.5% ( -13% - 17%) 0.832
LowSloppyPhrase 76.17 (5.5%) 76.57 (5.0%)
0.5% ( -9% - 11%) 0.754
AndHighHigh 236.71 (5.8%) 237.99 (5.2%)
0.5% ( -9% - 12%) 0.756
Fuzzy2 100.40 (5.6%) 101.02 (4.7%)
0.6% ( -9% - 11%) 0.708
OrHighHigh 154.05 (5.4%) 155.08 (5.0%)
0.7% ( -9% - 11%) 0.684
LowPhrase 327.86 (4.4%) 330.10 (4.9%)
0.7% ( -8% - 10%) 0.641
BrowseDayOfYearSSDVFacets 120.00 (5.1%) 120.88 (4.5%)
0.7% ( -8% - 10%) 0.627
MedTerm 2239.68 (6.3%) 2256.94 (5.9%)
0.8% ( -10% - 13%) 0.690
LowTerm 2516.56 (6.1%) 2537.04 (6.3%)
0.8% ( -10% - 14%) 0.679
OrHighMed 594.85 (6.7%) 599.76 (5.2%)
0.8% ( -10% - 13%) 0.664
MedSloppyPhrase 256.82 (5.2%) 259.03 (5.1%)
0.9% ( -9% - 11%) 0.601
PKLookup 221.95 (6.2%) 223.88 (5.6%)
0.9% ( -10% - 13%) 0.641
BrowseMonthSSDVFacets 135.72 (5.9%) 136.94 (5.4%)
0.9% ( -9% - 12%) 0.615
LowSpanNear 668.06 (6.4%) 674.95 (5.1%)
1.0% ( -9% - 13%) 0.572
AndHighLow 1603.74 (7.1%) 1621.34 (5.5%)
1.1% ( -10% - 14%) 0.585
HighTerm 1927.72 (5.4%) 1949.95 (6.6%)
1.2% ( -10% - 13%) 0.547
HighIntervalsOrdered 293.62 (5.8%) 297.01 (5.0%)
1.2% ( -9% - 12%) 0.501
HighPhrase 396.34 (5.4%) 401.03 (5.4%)
1.2% ( -9% - 12%) 0.491
Wildcard 749.60 (7.8%) 759.43 (8.9%)
1.3% ( -14% - 19%) 0.620
MedSpanNear 576.19 (5.8%) 584.48 (5.2%)
1.4% ( -9% - 13%) 0.407
BrowseDayOfYearTaxoFacets 32.34 (7.6%) 32.86 (8.0%)
1.6% ( -12% - 18%) 0.513
BrowseDateTaxoFacets 32.23 (7.7%) 32.76 (8.0%)
1.6% ( -13% - 18%) 0.512
OrHighLow 526.26 (6.7%) 536.54 (6.3%)
2.0% ( -10% - 16%) 0.342
BrowseMonthTaxoFacets 35.48 (9.1%) 36.21 (9.1%)
2.1% ( -14% - 22%) 0.474
HighTermMonthSort 349.19 (12.8%) 364.73 (14.0%)
4.5% ( -19% - 35%) 0.294
HighTermDayOfYearSort 690.75 (11.2%) 724.87 (11.0%)
4.9% ( -15% - 30%) 0.159
```
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