Accelerate Vector128<long>::op_Multiply on x64 - #103555

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EgorBo:arm-mul-64bit
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Accelerate Vector128<long>::op_Multiply on x64#103555
EgorBo merged 21 commits into
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EgorBo:arm-mul-64bit

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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)

publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;

Current codegen on x64 cpu without AVX512:

; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120

New codegen:

; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50

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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL

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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs Outdated
EgorBoand others added 2 commits June 17, 2024 17:01
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
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Update Vector128_1.cs
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128

usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}

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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128
MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00

BDN_Artifacts.zip

Flame graphs: Main vs PR 🔥
Hot asm: Main vs PR
Hot functions: Main vs PR

For clean perf results, make sure you have just one [Benchmark] in your app.

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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128
MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62

BDN_Artifacts.zip

Flame graphs: Main vs PR 🔥
Hot asm: Main vs PR
Hot functions: Main vs PR

For clean perf results, make sure you have just one [Benchmark] in your app.

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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128
MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00

BDN_Artifacts.zip

Flame graphs: Main vs PR 🔥
Hot asm: Main vs PR
Hot functions: Main vs PR

For clean perf results, make sure you have just one [Benchmark] in your app.

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/azp run runtime-coreclr jitstress-isas-x86

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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.
I've expanded it in importer similar to existing op_Multiply expansions

Benchmark improvement: #103555 (comment)

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Comment threadsrc/coreclr/jit/gentree.cpp Outdated
tannergooding
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Comment threadsrc/coreclr/jit/gentree.cpp
Comment on lines +21627 to +21631
// Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
GenTreeHWIntrinsic* tmp3 =
gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
CORINFO_TYPE_UINT, simdSize);

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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)

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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)

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Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
if (TARGET_POINTER_SIZE == 4)
{
// TODO-XARCH-CQ: We should support long/ulong multiplication
// TODO-XARCH-CQ: 32bit support

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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?

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Not sure to be honest, that check was pre-existing, I only changed comment

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Accelerate Vector128<long>::op_Multiply on x64 - #103555
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EgorBo merged 21 commits into
dotnet:mainfrom 
EgorBo:arm-mul-64bit
Jun 28, 2024
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Accelerate Vector128<long>::op_Multiply on x64#103555
EgorBo merged 21 commits into
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EgorBo:arm-mul-64bit
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
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Update Vector128_1.cs
57898f0
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp

Comment on lines

+21627
 to 
+21631

 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
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Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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Accelerate Vector128<long>::op_Multiply on x64 - #103555
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Accelerate Vector128<long>::op_Multiply on x64#103555
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

This was referenced Jun 20, 2024

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/azp run runtime-coreclr jitstress-isas-x86
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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp
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Comment threadsrc/coreclr/jit/gentree.cpp

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+21627
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 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
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Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Accelerate Vector128<long>::op_Multiply on x64 - #103555
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EgorBo:arm-mul-64bit
Jun 28, 2024
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Accelerate Vector128<long>::op_Multiply on x64#103555
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EgorBo:arm-mul-64bit
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
@neon-sunset
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
@EgorBo
Update Vector128_1.cs
57898f0
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
@EgorBot
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

This was referenced Jun 20, 2024

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/azp run runtime-coreclr jitstress-isas-x86
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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp
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Comment threadsrc/coreclr/jit/gentree.cpp

Comment on lines

+21627
 to 
+21631

 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
tannergooding
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reviewed
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View reviewed changes
Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Accelerate Vector128<long>::op_Multiply on x64 - #103555
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EgorBo:arm-mul-64bit
Jun 28, 2024
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Accelerate Vector128<long>::op_Multiply on x64#103555
EgorBo merged 21 commits into
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EgorBo:arm-mul-64bit
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
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Update Vector128_1.cs
57898f0
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

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/azp run runtime-coreclr jitstress-isas-x86
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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp
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Comment threadsrc/coreclr/jit/gentree.cpp

Comment on lines

+21627
 to 
+21631

 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
tannergooding
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View reviewed changes
Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Accelerate Vector128<long>::op_Multiply on x64 - #103555
Merged
EgorBo merged 21 commits into
dotnet:mainfrom 
EgorBo:arm-mul-64bit
Jun 28, 2024
Merged
Accelerate Vector128<long>::op_Multiply on x64#103555
EgorBo merged 21 commits into
dotnet:mainfrom 
EgorBo:arm-mul-64bit
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Tagging subscribers to this area: @dotnet/area-system-runtime-intrinsics

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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
@neon-sunset
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

This was referenced Jun 17, 2024

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
@EgorBo
Update Vector128_1.cs
57898f0
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
@EgorBot
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
@EgorBot
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

This was referenced Jun 20, 2024

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/azp run runtime-coreclr jitstress-isas-x86
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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp
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Comment threadsrc/coreclr/jit/gentree.cpp

Comment on lines

+21627
 to 
+21631

 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
tannergooding
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reviewed
Jun 27, 2024
View reviewed changes
Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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Accelerate Vector128<long>::op_Multiply on x64 - #103555
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EgorBo merged 21 commits into
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EgorBo:arm-mul-64bit
Jun 28, 2024
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Accelerate Vector128<long>::op_Multiply on x64#103555
EgorBo merged 21 commits into
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EgorBo:arm-mul-64bit
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
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Update Vector128_1.cs
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

This was referenced Jun 20, 2024

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/azp run runtime-coreclr jitstress-isas-x86
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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp
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Comment threadsrc/coreclr/jit/gentree.cpp

Comment on lines

+21627
 to 
+21631

 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
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Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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Skip to content
Accelerate Vector128<long>::op_Multiply on x64 - #103555
Merged
EgorBo merged 21 commits into
dotnet:mainfrom 
EgorBo:arm-mul-64bit
Jun 28, 2024
Merged
Accelerate Vector128<long>::op_Multiply on x64#103555
EgorBo merged 21 commits into
dotnet:mainfrom 
EgorBo:arm-mul-64bit
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This PR optimizes Vector128 and Vector256 multiplication for long/ulong when AVX512 is not presented in the system. It makes XxHash128 faster, see #103555 (comment)
publicVector128<long>Foo(Vector128<long>a,Vector128<long>b)=>a*b;
Current codegen on x64 cpu without AVX512:
; Method MyBench:Foopushrsipushrbxsubrsp,104movrbx,rdxmovrdx, qword ptr [r8]mov qword ptr [rsp+0x58],rdxmovrdx, qword ptr [r9]mov qword ptr [rsp+0x50],rdxmovrdx, qword ptr [rsp+0x58]imulrdx, qword ptr [rsp+0x50]mov qword ptr [rsp+0x60],rdxmovrsi, qword ptr [rsp+0x60]movrdx, qword ptr [r8+0x08]mov qword ptr [rsp+0x40],rdxmovrdx, qword ptr [r9+0x08]mov qword ptr [rsp+0x38],rdxmovrcx, qword ptr [rsp+0x40]movrdx, qword ptr [rsp+0x38]call[System.Runtime.Intrinsics.Scalar`1[long]:Multiply(long,long):long] ;;; not inlined call!mov qword ptr [rsp+0x48],raxmovrax, qword ptr [rsp+0x48]mov qword ptr [rsp+0x20],rsimov qword ptr [rsp+0x28],rax vmovaps xmm0, xmmword ptr [rsp+0x20]vmovups xmmword ptr [rbx],xmm0movrax,rbxaddrsp,104poprbxpoprsiret; Total bytes of code: 120
New codegen:
; Method MyBench:Foovmovupsxmm0, xmmword ptr [r8]vmovupsxmm1, xmmword ptr [r9] vpmuludq xmm2,xmm1,xmm0 vpshufd xmm1,xmm1,-79 vpmulld xmm0,xmm1,xmm0 vxorps xmm1,xmm1,xmm1vphadddxmm0,xmm0,xmm1 vpshufd xmm0,xmm0,115vpaddqxmm0,xmm0,xmm2vmovups xmmword ptr [rdx],xmm0movrax,rdxret; Total bytes of code: 50
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Note: results should be better if we do it in JIT, it will enable loop hoisting, cse, etc for MUL
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Note #103539 (comment) (and https://godbolt.org/z/eqsrf341M) from xxHash128 issue.

This was referenced Jun 17, 2024

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Comment threadsrc/libraries/System.Private.CoreLib/src/System/Runtime/Intrinsics/Vector128_1.cs
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Update src/libraries/System.Private.CoreLib/src/System/Runtime/Intrin…
49a359f
…sics/Vector128_1.cs
Co-authored-by: Tanner Gooding <tagoo@outlook.com>
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Update Vector128_1.cs
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@EgorBot -amd -intel -arm64 -profiler --envvars DOTNET_PreferredVectorBitWidth:128
usingSystem.IO.Hashing;usingBenchmarkDotNet.Attributes;publicclassBench{staticreadonlybyte[]Data=newbyte[1000000];[Benchmark]publicbyte[]BenchXxHash128(){XxHash128hash=new();hash.Append(Data);returnhash.GetHashAndReset();}}
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Benchmark results on Intel
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Intel Xeon Platinum 8370C CPU 2.80GHz, 1 CPU, 16 logical and 8 physical cores
Job-ITXSAG : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
Job-XSORFZ : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX-512F+CD+BW+DQ+VL+VBMI
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main43.41 μs0.087 μs1.00
BenchXxHash128PR43.33 μs0.009 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
@EgorBot
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Benchmark results on Amd
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
AMD EPYC 7763, 1 CPU, 16 logical and 8 physical cores
Job-SUBLYH : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
Job-OPUYDY : .NET 9.0.0 (42.42.42.42424), X64 RyuJIT AVX2
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main71.20 μs0.022 μs1.00
BenchXxHash128PR43.84 μs0.013 μs0.62
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.
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Benchmark results on Arm64
BenchmarkDotNet v0.13.12, Ubuntu 22.04.4 LTS (Jammy Jellyfish)
Unknown processor
Job-EDPWDU : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
Job-TIALUR : .NET 9.0.0 (42.42.42.42424), Arm64 RyuJIT AdvSIMD
EnvironmentVariables=DOTNET_PreferredVectorBitWidth=128

MethodToolchainMeanErrorRatio
BenchXxHash128Main116.9 μs0.11 μs1.00
BenchXxHash128PR116.8 μs0.07 μs1.00
BDN_Artifacts.zip
Flame graphs: Main vs PR 🔥

Hot asm: Main vs PR

Hot functions: Main vs PR
For clean perf results, make sure you have just one [Benchmark] in your app.

This was referenced Jun 20, 2024

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/azp run runtime-coreclr jitstress-isas-x86
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@tannergooding PTAL, I'll add arm64 separately, need to test different impls.

I've expanded it in importer similar to existing op_Multiply expansions
Benchmark improvement: #103555 (comment)
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Comment threadsrc/coreclr/jit/gentree.cpp
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Comment threadsrc/coreclr/jit/gentree.cpp

Comment on lines

+21627
 to 
+21631

 // Vector256<int> tmp3 = Avx2.HorizontalAdd(tmp2.AsInt32(), Vector256<int>.Zero);
 GenTreeHWIntrinsic* tmp3 =
 gtNewSimdHWIntrinsicNode(type, tmp2, gtNewZeroConNode(type),
 is256 ? NI_AVX2_HorizontalAdd : NI_SSSE3_HorizontalAdd,
 CORINFO_TYPE_UINT, simdSize);
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I know in other places we've started avoiding hadd in favor of shuffle+add, might be worth seeing if that's appropriate here too (low priority, non blocking)
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I tried to benchmark different implementations for it and they all were equaly fast e.g. #99871 (comment)
tannergooding
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View reviewed changes
Comment threadsrc/coreclr/jit/hwintrinsicxarch.cpp
 if (TARGET_POINTER_SIZE == 4)
 {
 // TODO-XARCH-CQ: We should support long/ulong multiplication
 // TODO-XARCH-CQ: 32bit support
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What's blocking 32-bit support? It doesn't look like we're using any _X64 intrinsics in the fallback logic?
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Not sure to be honest, that check was pre-existing, I only changed comment
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merged commit 33ca32d
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June 28, 2024 21:40
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