On Fri, 11 Nov 2022 13:00:06 GMT, Claes Redestad <redes...@openjdk.org> wrote:
>> Continuing the work initiated by @luhenry to unroll and then intrinsify >> polynomial hash loops. >> >> I've rewired the library changes to route via a single `@IntrinsicCandidate` >> method. To make this work I've harmonized how they are invoked so that >> there's less special handling and checks in the intrinsic. Mainly do the >> null-check outside of the intrinsic for `Arrays.hashCode` cases. >> >> Having a centralized entry point means it'll be easier to parameterize the >> factor and start values which are now hard-coded (always 31, and a start >> value of either one for `Arrays` or zero for `String`). It seems somewhat >> premature to parameterize this up front. >> >> The current implementation is performance neutral on microbenchmarks on all >> tested platforms (x64, aarch64) when not enabling the intrinsic. We do add a >> few trivial method calls which increase the call stack depth, so surprises >> cannot be ruled out on complex workloads. >> >> With the most recent fixes the x64 intrinsic results on my workstation look >> like this: >> >> Benchmark (size) Mode Cnt Score Error >> Units >> StringHashCode.Algorithm.defaultLatin1 1 avgt 5 2.199 ± 0.017 >> ns/op >> StringHashCode.Algorithm.defaultLatin1 10 avgt 5 6.933 ± 0.049 >> ns/op >> StringHashCode.Algorithm.defaultLatin1 100 avgt 5 29.935 ± 0.221 >> ns/op >> StringHashCode.Algorithm.defaultLatin1 10000 avgt 5 1596.982 ± 7.020 >> ns/op >> >> Baseline: >> >> Benchmark (size) Mode Cnt Score Error >> Units >> StringHashCode.Algorithm.defaultLatin1 1 avgt 5 2.200 ± 0.013 >> ns/op >> StringHashCode.Algorithm.defaultLatin1 10 avgt 5 9.424 ± 0.122 >> ns/op >> StringHashCode.Algorithm.defaultLatin1 100 avgt 5 90.541 ± 0.512 >> ns/op >> StringHashCode.Algorithm.defaultLatin1 10000 avgt 5 9425.321 ± 67.630 >> ns/op >> >> I.e. no measurable overhead compared to baseline even for `size == 1`. >> >> The vectorized code now nominally works for all unsigned cases as well as >> ints, though more testing would be good. >> >> Benchmark for `Arrays.hashCode`: >> >> Benchmark (size) Mode Cnt Score Error Units >> ArraysHashCode.bytes 1 avgt 5 1.884 ± 0.013 ns/op >> ArraysHashCode.bytes 10 avgt 5 6.955 ± 0.040 ns/op >> ArraysHashCode.bytes 100 avgt 5 87.218 ± 0.595 ns/op >> ArraysHashCode.bytes 10000 avgt 5 9419.591 ± 38.308 ns/op >> ArraysHashCode.chars 1 avgt 5 2.200 ± 0.010 ns/op >> ArraysHashCode.chars 10 avgt 5 6.935 ± 0.034 ns/op >> ArraysHashCode.chars 100 avgt 5 30.216 ± 0.134 ns/op >> ArraysHashCode.chars 10000 avgt 5 1601.629 ± 6.418 ns/op >> ArraysHashCode.ints 1 avgt 5 2.200 ± 0.007 ns/op >> ArraysHashCode.ints 10 avgt 5 6.936 ± 0.034 ns/op >> ArraysHashCode.ints 100 avgt 5 29.412 ± 0.268 ns/op >> ArraysHashCode.ints 10000 avgt 5 1610.578 ± 7.785 ns/op >> ArraysHashCode.shorts 1 avgt 5 1.885 ± 0.012 ns/op >> ArraysHashCode.shorts 10 avgt 5 6.961 ± 0.034 ns/op >> ArraysHashCode.shorts 100 avgt 5 87.095 ± 0.417 ns/op >> ArraysHashCode.shorts 10000 avgt 5 9420.617 ± 50.089 ns/op >> >> Baseline: >> >> Benchmark (size) Mode Cnt Score Error Units >> ArraysHashCode.bytes 1 avgt 5 3.213 ± 0.207 ns/op >> ArraysHashCode.bytes 10 avgt 5 8.483 ± 0.040 ns/op >> ArraysHashCode.bytes 100 avgt 5 90.315 ± 0.655 ns/op >> ArraysHashCode.bytes 10000 avgt 5 9422.094 ± 62.402 ns/op >> ArraysHashCode.chars 1 avgt 5 3.040 ± 0.066 ns/op >> ArraysHashCode.chars 10 avgt 5 8.497 ± 0.074 ns/op >> ArraysHashCode.chars 100 avgt 5 90.074 ± 0.387 ns/op >> ArraysHashCode.chars 10000 avgt 5 9420.474 ± 41.619 ns/op >> ArraysHashCode.ints 1 avgt 5 2.827 ± 0.019 ns/op >> ArraysHashCode.ints 10 avgt 5 7.727 ± 0.043 ns/op >> ArraysHashCode.ints 100 avgt 5 89.405 ± 0.593 ns/op >> ArraysHashCode.ints 10000 avgt 5 9426.539 ± 51.308 ns/op >> ArraysHashCode.shorts 1 avgt 5 3.071 ± 0.062 ns/op >> ArraysHashCode.shorts 10 avgt 5 8.168 ± 0.049 ns/op >> ArraysHashCode.shorts 100 avgt 5 90.399 ± 0.292 ns/op >> ArraysHashCode.shorts 10000 avgt 5 9420.171 ± 44.474 ns/op >> >> >> As we can see the `Arrays` intrinsics are faster for small inputs, and >> faster on large inputs for `char` and `int` (the ones currently vectorized). >> I aim to fix `byte` and `short` cases before integrating, though it might be >> acceptable to hand that off as follow-up enhancements to not further delay >> integration of this enhancement. > > Claes Redestad has updated the pull request incrementally with one additional > commit since the last revision: > > Missing & 0xff in StringLatin1::hashCode Out of curiosity: how does this intrinsic affect time-to-safepoint? Does it matter? I don't see any safepoint poll, but then I don't precisely know how safepoints work, so I could be missing something. Theoretically, with 2^31 elements count limit in Java, the whole computation is always a fraction of a second, but maybe it would matter with e.g. ZGC, which could ask for a safepoint while the thread is hashing an array with 2 billion ints. > 1. expanding `array` input into `base`, `offset`, and `length`. It will make > it applicable to any type of data source (on-heap/off-heap `ByteBuffer`s, > `MemorySegment`s. There could be memory-mapped ByteBuffers and MemorySegments and that would make the whole hashing operation much more prone to be exceedingly long and therefore possibly dramatically affecting time-to-safepoint. Again, this could be misunderstanding on my side, but I'm curious how safepoints interplay with this intrinsic. Also, even without memory mapping, MemorySegments can be much larger than 2^31 elements in size, so hashing a huge MemorySegment could take much longer than hashing even the biggest ordinary (limited by 31-bit indexing) array of primitives. ------------- PR: https://git.openjdk.org/jdk/pull/10847
