On Fri, 11 Nov 2022 13:00:06 GMT, Claes Redestad <[email protected]> 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
src/hotspot/cpu/x86/stubRoutines_x86.cpp line 230:
> 228: #endif // _LP64
> 229:
> 230: jint StubRoutines::x86::_arrays_hashcode_powers_of_31[] =
This should be declared only for LP64.
src/hotspot/cpu/x86/vm_version_x86.cpp line 1671:
> 1669: }
> 1670: if (UseAVX >= 2) {
> 1671: FLAG_SET_ERGO_IF_DEFAULT(UseVectorizedHashCodeIntrinsic, true);
This could be just FLAG_SET_DEFAULT instead of FLAG_SET_ERGO_IF_DEFAULT.
-------------
PR: https://git.openjdk.org/jdk/pull/10847
