RFR: 8282664: Unroll by hand StringUTF16 and StringLatin1 polynomial hash loops

[Quan Anh Mai]

On Tue, 25 Oct 2022 10:37:40 GMT, Claes Redestad <redestad at 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.

I am planning to submit that patch after finishing with the current under-reviewed PRs. That patch was stalled because there was no node for vectorised unsigned cast and constant values. The first one has been added and the second one may be worked around as in the PR. I also thought of using masked loads for tail processing instead of falling back to scalar implementation.

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PR: https://git.openjdk.org/jdk/pull/10847