On Tue, 15 Oct 2024 17:00:26 GMT, Jasmine Karthikeyan <jkarthike...@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
