RFR: 8282664: Unroll by hand StringUTF16 and StringLatin1 polynomial hash loops [v13]
Piotr Tarsa
duke at openjdk.org
Sat Nov 12 15:29:21 UTC 2022
On Fri, 11 Nov 2022 13:00:06 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:
>
> 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
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