RFR: 8343689: AArch64: Optimize MulReduction implementation [v7]

[Mikhail Ablakatov]

> 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:

 - Compare VL against MaxVectorSize instead of FloatRegister::sve_vl_max
 - Use a dedicated ptrue predicate register
   
   This shifts MulReduction performance on Neoverse V1 a bit. Here Before
   if before this specific commit (ebad6dd37e332da44222c50cd17c69f3ff3f0635)
   and After is this commit.
   
   | Benchmark                | Before (ops/ms) | After (ops/ms) | Diff (%) |
   | ------------------------ | --------------- | -------------- | -------- |
   | ByteMaxVector.MULLanes   | 9883.151        | 9093.557       | -7.99%   |
   | DoubleMaxVector.MULLanes | 2712.674        | 2607.367       | -3.89%   |
   | FloatMaxVector.MULLanes  | 3388.811        | 3291.429       | -2.88%   |
   | IntMaxVector.MULLanes    | 4765.554        | 5031.741       | +5.58%   |
   | LongMaxVector.MULLanes   | 2685.228        | 2896.445       | +7.88%   |
   | ShortMaxVector.MULLanes  | 5128.185        | 5197.656       | +1.35%   |

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

Changes:
  - all: https://git.openjdk.org/jdk/pull/23181/files
  - new: https://git.openjdk.org/jdk/pull/23181/files/ebad6dd3..d35f1089

Webrevs:
 - full: https://webrevs.openjdk.org/?repo=jdk&pr=23181&range=06
 - incr: https://webrevs.openjdk.org/?repo=jdk&pr=23181&range=05-06

  Stats: 69 lines in 4 files changed: 12 ins; 17 del; 40 mod
  Patch: https://git.openjdk.org/jdk/pull/23181.diff
  Fetch: git fetch https://git.openjdk.org/jdk.git pull/23181/head:pull/23181

PR: https://git.openjdk.org/jdk/pull/23181