RFR: 8341137: Optimize long vector multiplication using x86 VPMUL[U]DQ instruction

[Quan Anh Mai]

On Tue, 15 Oct 2024 17:00:26 GMT, Jasmine Karthikeyan <jkarthikeyan at openjdk.org> wrote:

>> This patch optimizes LongVector multiplication by inferring VPMUL[U]DQ instruction for following IR pallets.
>>   
>> 
>>        MulVL   ( AndV  SRC1,  0xFFFFFFFF)   ( AndV  SRC2,  0xFFFFFFFF) 
>>        MulVL   (URShiftVL SRC1 , 32) (URShiftVL SRC2, 32)
>>        MulVL   (URShiftVL SRC1 , 32)  ( AndV  SRC2,  0xFFFFFFFF)
>>        MulVL   ( AndV  SRC1,  0xFFFFFFFF) (URShiftVL SRC2 , 32)
>>        MulVL   (VectorCastI2X SRC1) (VectorCastI2X SRC2)
>>        MulVL   (RShiftVL SRC1 , 32) (RShiftVL SRC2, 32)
>> 
>> 
>> 
>>  A  64x64 bit multiplication produces 128 bit result, and can be performed by individually multiplying upper and lower double word of multiplier with multiplicand and assembling the partial products to compute full width result. Targets supporting vector quadword multiplication have separate instructions to compute upper and lower quadwords for 128 bit result. Therefore existing VectorAPI multiplication operator expects shape conformance between source and result vectors.
>> 
>> If upper 32 bits of quadword multiplier and multiplicand is always set to zero then result of multiplication is only dependent on the partial product of their lower double words and can be performed using unsigned 32 bit multiplication instruction with quadword saturation. Patch matches this pattern in a target dependent manner without introducing new IR node.
>>  
>> VPMUL[U]DQ instruction performs [unsigned] multiplication between even numbered doubleword lanes of two long vectors and produces 64 bit result.  It has much lower latency compared to full 64 bit multiplication instruction "VPMULLQ", in addition non-AVX512DQ targets does not support direct quadword multiplication, thus we can save redundant partial product for zeroed out upper 32 bits. This results into throughput improvements on both P and E core Xeons.
>> 
>> Please find below the performance of [XXH3 hashing benchmark ](https://mail.openjdk.org/pipermail/panama-dev/2024-July/020557.html)included with the patch:-
>>  
>> 
>> Sierra Forest :-
>> ============
>> Baseline:-
>> Benchmark                                 (SIZE)   Mode  Cnt    Score   Error   Units
>> VectorXXH3HashingBenchmark.hashingKernel    1024  thrpt    2  806.228          ops/ms
>> VectorXXH3HashingBenchmark.hashingKernel    2048  thrpt    2  403.044          ops/ms
>> VectorXXH3HashingBenchmark.hashingKernel    4096  thrpt    2  200.641          ops/ms
>> VectorXXH3HashingBenchmark.hashingKernel    8192  thrpt    2  100.664          ops/ms
>> 
>> With Optimizati...
>
> I'm pretty ambivalent, I think implementing it either way would be alright. Especially with unit tests, I think the lowering implementation wouldn't be that difficult. Maybe another reviewer has an opinion?
> 
> About PhaseLowering though, I've found some more interesting things we could do with it, especially with improving vectorization support in the backend. @merykitty have you already started to work on it? I was thinking about prototyping it soon. Just wanted to make sure we're not doing the same work twice :)

@jaskarth Please proceed with it, I have a really simple prototype for it but I don't have any plan to proceed further soon. Thanks a lot :)

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

PR Comment: https://git.openjdk.org/jdk/pull/21244#issuecomment-2414605470