RFR: 8282664: Unroll by hand StringUTF16 and StringLatin1 polynomial hash loops [v4]
Claes Redestad
redestad at openjdk.org
Mon Oct 31 22:10:30 UTC 2022
On Mon, 31 Oct 2022 21:48:37 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.
>
> Claes Redestad has updated the pull request incrementally with one additional commit since the last revision:
>
> Change scalar unroll to 2 element stride, minding dependency chain
A stride of 2 allows small element cases to perform a bit better, while also performing better than before on longer arrays for the `byte` and `short` cases that don't get any benefit from vectorization:
Benchmark (size) Mode Cnt Score Error Units
ArraysHashCode.bytes 1 avgt 5 1.414 ± 0.005 ns/op
ArraysHashCode.bytes 10 avgt 5 6.908 ± 0.020 ns/op
ArraysHashCode.bytes 100 avgt 5 73.666 ± 0.390 ns/op
ArraysHashCode.bytes 10000 avgt 5 7846.994 ± 53.628 ns/op
ArraysHashCode.chars 1 avgt 5 1.414 ± 0.007 ns/op
ArraysHashCode.chars 10 avgt 5 7.229 ± 0.044 ns/op
ArraysHashCode.chars 100 avgt 5 30.718 ± 0.229 ns/op
ArraysHashCode.chars 10000 avgt 5 1621.463 ± 116.286 ns/op
ArraysHashCode.ints 1 avgt 5 1.414 ± 0.008 ns/op
ArraysHashCode.ints 10 avgt 5 7.540 ± 0.042 ns/op
ArraysHashCode.ints 100 avgt 5 29.429 ± 0.121 ns/op
ArraysHashCode.ints 10000 avgt 5 1600.855 ± 9.274 ns/op
ArraysHashCode.shorts 1 avgt 5 1.414 ± 0.010 ns/op
ArraysHashCode.shorts 10 avgt 5 6.914 ± 0.045 ns/op
ArraysHashCode.shorts 100 avgt 5 73.684 ± 0.501 ns/op
ArraysHashCode.shorts 10000 avgt 5 7846.829 ± 49.984 ns/op
I've also made some changes to improve the String cases, which can avoid the first coeff*h multiplication on first pass. This gets the size 1 latin1 case down to 1.1ns/op without penalizing the empty case. We're now improving over the baseline on almost all* tested sizes:
Benchmark (size) Mode Cnt Score Error Units
StringHashCode.Algorithm.defaultLatin1 0 avgt 5 0.946 ± 0.005 ns/op
StringHashCode.Algorithm.defaultLatin1 1 avgt 5 1.108 ± 0.003 ns/op
StringHashCode.Algorithm.defaultLatin1 2 avgt 5 2.042 ± 0.005 ns/op
StringHashCode.Algorithm.defaultLatin1 31 avgt 5 18.636 ± 0.286 ns/op
StringHashCode.Algorithm.defaultLatin1 32 avgt 5 15.938 ± 1.086 ns/op
StringHashCode.Algorithm.defaultUTF16 0 avgt 5 1.257 ± 0.004 ns/op
StringHashCode.Algorithm.defaultUTF16 1 avgt 5 2.198 ± 0.005 ns/op
StringHashCode.Algorithm.defaultUTF16 2 avgt 5 2.559 ± 0.011 ns/op
StringHashCode.Algorithm.defaultUTF16 31 avgt 5 15.754 ± 0.036 ns/op
StringHashCode.Algorithm.defaultUTF16 32 avgt 5 16.616 ± 0.042 ns/op
Baseline:
Benchmark (size) Mode Cnt Score Error Units
StringHashCode.Algorithm.defaultLatin1 0 avgt 5 0.942 ± 0.005 ns/op
StringHashCode.Algorithm.defaultLatin1 1 avgt 5 1.991 ± 0.013 ns/op
StringHashCode.Algorithm.defaultLatin1 2 avgt 5 2.831 ± 0.021 ns/op
StringHashCode.Algorithm.defaultLatin1 31 avgt 5 25.042 ± 0.112 ns/op
StringHashCode.Algorithm.defaultLatin1 32 avgt 5 25.857 ± 0.133 ns/op
StringHashCode.Algorithm.defaultUTF16 0 avgt 5 0.789 ± 0.006 ns/op
StringHashCode.Algorithm.defaultUTF16 1 avgt 5 3.459 ± 0.007 ns/op
StringHashCode.Algorithm.defaultUTF16 2 avgt 5 4.400 ± 0.010 ns/op
StringHashCode.Algorithm.defaultUTF16 31 avgt 5 25.721 ± 0.067 ns/op
StringHashCode.Algorithm.defaultUTF16 32 avgt 5 27.162 ± 0.093 ns/op
There's a negligible regression on `defaultUTF16` for size = 0 due moving the length shift up earlier. This can only happen when running with CompactStrings disabled. And even if you were the change significantly helps improve size 1-31, which should more than make up for a small cost increase hashing empty strings.
-------------
PR: https://git.openjdk.org/jdk/pull/10847
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