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 I'm getting pulled into other tasks and would request for this to be either accepted as-is, rejected or picked up by someone else to rewrite it to something that can be accepted. Obviously I'm biased towards acceptance: While imperfect, it provides improved testing - both functional and performance-wise - and establishes a significantly improved benchmark for more future-proof solutions to beat. There are many ways to iteratively improve upon this solution, some of which would even simplify the implementation. But in the face of upcoming changes that might allow C2 to optimize these kinds of loops without intrinsic support I am not sure spending more time on perfecting the current patch is worth our while. Rejecting it might be the reasonable thing to do, too, especially if the C2 loop optimizations @iwanowww points out might be coming around sooner rather than later. Even if that's not coming soon, the PR at hand adds a chunk of complexity for the compiler team to maintain. ------------- PR: https://git.openjdk.org/jdk/pull/10847
