RFR: 8282664: Unroll by hand StringUTF16 and StringLatin1 polynomial hash loops [v5]
Claes Redestad
redestad at openjdk.org
Mon Nov 7 13:58:14 UTC 2022
> 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 with a new target base due to a merge or a rebase. The pull request now contains 43 commits:
- Merge branch 'master' into 8282664-polyhash
- Merge branch 'master' into 8282664-polyhash
- Change scalar unroll to 2 element stride, minding dependency chain
- Require UseSSE >= 3 due transitive use of sse3 instructions from ReduceI
- Reorder loops and some other suggestions from @merykitty
- ws
- Add ArraysHashCode microbenchmarks
- Fixed vector loops for int and char arrays
- Split up Arrays/HashCode tests
- Fixes, optimized short inputs, temporarily disabled vector loop for Arrays.hashCode cases, added and improved tests
- ... and 33 more: https://git.openjdk.org/jdk/compare/d634ddef...95c10b5f
-------------
Changes: https://git.openjdk.org/jdk/pull/10847/files
Webrev: https://webrevs.openjdk.org/?repo=jdk&pr=10847&range=04
Stats: 1151 lines in 32 files changed: 1093 ins; 32 del; 26 mod
Patch: https://git.openjdk.org/jdk/pull/10847.diff
Fetch: git fetch https://git.openjdk.org/jdk pull/10847/head:pull/10847
PR: https://git.openjdk.org/jdk/pull/10847
More information about the core-libs-dev
mailing list