RFR: 8343689: AArch64: Optimize MulReduction implementation [v2]

[Andrew Haley]

On Wed, 5 Feb 2025 11:20:59 GMT, Mikhail Ablakatov <mablakatov at openjdk.org> wrote:

>> Add a reduce_mul intrinsic SVE specialization for >= 256-bit long vectors. It multiplies halves of the source vector using SVE instructions to get to a 128-bit long vector that fits into a SIMD&FP register. After that point, existing ASIMD implementation is used.
>> 
>> Nothing changes for <= 128-bit long vectors as for those the existing ASIMD implementation is used directly still.
>> 
>> The benchmarks below are from [panama-vector/vectorIntrinsics:test/micro/org/openjdk/bench/jdk/incubator/vector/operation](https://github.com/openjdk/panama-vector/tree/vectorIntrinsics/test/micro/org/openjdk/bench/jdk/incubator/vector/operation). To the best of my knowledge, openjdk/jdk is missing VectorAPI reducion micro-benchmarks.
>> 
>> Benchmarks results:
>> 
>> Neoverse-V1 (SVE 256-bit)
>> 
>>   Benchmark                 (size)   Mode   master         PR  Units
>>   ByteMaxVector.MULLanes      1024  thrpt 5447.643  11455.535 ops/ms
>>   ShortMaxVector.MULLanes     1024  thrpt 3388.183   7144.301 ops/ms
>>   IntMaxVector.MULLanes       1024  thrpt 3010.974   4911.485 ops/ms
>>   LongMaxVector.MULLanes      1024  thrpt 1539.137   2562.835 ops/ms
>>   FloatMaxVector.MULLanes     1024  thrpt 1355.551   4158.128 ops/ms
>>   DoubleMaxVector.MULLanes    1024  thrpt 1715.854   3284.189 ops/ms
>> 
>> 
>> Fujitsu A64FX (SVE 512-bit):
>> 
>>   Benchmark                 (size)   Mode   master         PR  Units
>>   ByteMaxVector.MULLanes      1024  thrpt 1091.692   2887.798 ops/ms
>>   ShortMaxVector.MULLanes     1024  thrpt  597.008   1863.338 ops/ms
>>   IntMaxVector.MULLanes       1024  thrpt  510.642   1348.651 ops/ms
>>   LongMaxVector.MULLanes      1024  thrpt  468.878    878.620 ops/ms
>>   FloatMaxVector.MULLanes     1024  thrpt  376.284   2237.564 ops/ms
>>   DoubleMaxVector.MULLanes    1024  thrpt  431.343   1646.792 ops/ms
>
> Mikhail Ablakatov has updated the pull request incrementally with one additional commit since the last revision:
> 
>   Use EXT instead of COMPACT to split a vector into two halves
>   
>   Benchmarks results:
>   
>   Neoverse-V1 (SVE 256-bit)
>   
>     Benchmark                 (size)   Mode   master         PR  Units
>     ByteMaxVector.MULLanes      1024  thrpt 5447.643  11455.535 ops/ms
>     ShortMaxVector.MULLanes     1024  thrpt 3388.183   7144.301 ops/ms
>     IntMaxVector.MULLanes       1024  thrpt 3010.974   4911.485 ops/ms
>     LongMaxVector.MULLanes      1024  thrpt 1539.137   2562.835 ops/ms
>     FloatMaxVector.MULLanes     1024  thrpt 1355.551   4158.128 ops/ms
>     DoubleMaxVector.MULLanes    1024  thrpt 1715.854   3284.189 ops/ms
>   
>   Fujitsu A64FX (SVE 512-bit)
>   
>     Benchmark                 (size)   Mode   master         PR  Units
>     ByteMaxVector.MULLanes      1024  thrpt 1091.692   2887.798 ops/ms
>     ShortMaxVector.MULLanes     1024  thrpt  597.008   1863.338 ops/ms
>     IntMaxVector.MULLanes       1024  thrpt  510.642   1348.651 ops/ms
>     LongMaxVector.MULLanes      1024  thrpt  468.878    878.620 ops/ms
>     FloatMaxVector.MULLanes     1024  thrpt  376.284   2237.564 ops/ms
>     DoubleMaxVector.MULLanes    1024  thrpt  431.343   1646.792 ops/ms

src/hotspot/cpu/aarch64/aarch64_vector_ad.m4 line 1915:

> 1913: %}
> 1914: 
> 1915: instruct reduce_mulD(vRegD dst, vRegD dsrc, vReg vsrc, vReg tmp) %{

Please consider that `reduce_mulF_gt128b` and `reduce_mulD_gt128b` might be similar enough that they should be combined in the same way as other patterns in this file.

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PR Review Comment: https://git.openjdk.org/jdk/pull/23181#discussion_r1943420223