RFR: 8343689: AArch64: Optimize MulReduction implementation [v3]

[Xiaohong Gong]

On Wed, 26 Feb 2025 14:54:45 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 two additional commits since the last revision:
> 
>  - fixup: don't modify the value in vsrc
>    
>    Fix reduce_mul_integral_gt128b() so it doesn't modify vsrc. With this
>    change, the result of recursive folding is held in vtmp1. To be able to
>    pass this intermediate result to reduce_mul_integral_le128b(), we would
>    have to use another temporary FloatRegister, as vtmp1 would essentially
>    act as vsrc. It's possible to get around this however:
>    reduce_mul_integral_le128b() is modified so it's possible to pass
>    matching vsrc and vtmp2 arguments. By doing this, we save ourselves a
>    temporary register in rules that match to reduce_mul_integral_gt128b().
>  - cleanup: revert an unnecessary change to reduce_mul_fp_le128b() formating

src/hotspot/cpu/aarch64/aarch64_vector.ad line 3012:

> 3010:                             vReg tmp1, vReg tmp2) %{
> 3011:   predicate(Matcher::vector_length_in_bytes(n->in(2)) == 8 ||
> 3012:             Matcher::vector_length_in_bytes(n->in(2)) == 16);

Suggestion:

  predicate(Matcher::vector_length_in_bytes(n->in(2)) <= 16);

src/hotspot/cpu/aarch64/c2_MacroAssembler_aarch64.cpp line 2113:

> 2111:   while (vector_length_in_bytes > FloatRegister::neon_vl) {
> 2112:     do_recursive_folding_iteration(vtmp1, vtmp1, vtmp2);
> 2113:   }

Looks a little complex. Could we just simplify with following change? BTW, `sve_movprfx` inside of the loop can be saved. Please correct me if any mis-understanding!

Suggestion:


  sve_movprfx(vtmp2, vsrc);
  while (vector_length_in_bytes > FloatRegister::neon_vl) {
    unsigned vector_length = vector_length_in_bytes / type2aelembytes(bt);
    sve_gen_mask_imm(pgtmp, bt, vector_length / 2);
    // Shuffle the upper half elements of the register to the right.
    sve_ext(vtmp1, vtmp2, vector_length_in_bytes / 2);
    sve_mul(vtmp2, elemType_to_regVariant(bt), pgtmp, vtmp1);
    vector_length_in_bytes = vector_length_in_bytes / 2;
  }

-------------

PR Review Comment: https://git.openjdk.org/jdk/pull/23181#discussion_r1972770786
PR Review Comment: https://git.openjdk.org/jdk/pull/23181#discussion_r1972768005