Add vector support to System.Numerics.Tensors.TensorPrimitives.LeadingZeroCount for Byte and Int16 #110333

alexcovington · 2024-12-02T22:04:18Z

This PR adds vector support for integer types of size Byte and Int16 to System.Numerics.Tensors.TensorPrimitives.LeadingZeroCount.

To verify there is a performance improvement, I ran against the existing microbenchmarks here. This does not currently include coverage for Int16, so I built a version locally that included short.

On my AMD64 system, I see the following improvements:

Baseline

| Type                                      | Method           | BufferLength | Mean      | Error    | StdDev   | Median    | Min       | Max       | Allocated |
|------------------------------------------ |----------------- |------------- |----------:|---------:|---------:|----------:|----------:|----------:|----------:|
| Perf_BinaryIntegerTensorPrimitives<Byte>  | LeadingZeroCount | 128          |  34.38 ns | 0.163 ns | 0.136 ns |  34.40 ns |  34.14 ns |  34.62 ns |         - |
| Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | 128          |  52.31 ns | 1.148 ns | 1.322 ns |  51.66 ns |  50.78 ns |  54.56 ns |         - |
| Perf_BinaryIntegerTensorPrimitives<Byte>  | LeadingZeroCount | 3079         | 702.93 ns | 2.372 ns | 2.218 ns | 703.63 ns | 699.32 ns | 706.21 ns |         - |
| Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | 3079         | 793.33 ns | 4.117 ns | 3.650 ns | 793.13 ns | 788.37 ns | 800.02 ns |         - |

Diff

| Type                                      | Method           | BufferLength | Mean      | Error     | StdDev    | Median    | Min       | Max       | Allocated |
|------------------------------------------ |----------------- |------------- |----------:|----------:|----------:|----------:|----------:|----------:|----------:|
| Perf_BinaryIntegerTensorPrimitives<Byte>  | LeadingZeroCount | 128          |  6.407 ns | 0.0571 ns | 0.0534 ns |  6.394 ns |  6.344 ns |  6.509 ns |         - |
| Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | 128          | 10.175 ns | 0.0432 ns | 0.0361 ns | 10.184 ns | 10.107 ns | 10.224 ns |         - |
| Perf_BinaryIntegerTensorPrimitives<Byte>  | LeadingZeroCount | 3079         | 24.931 ns | 0.1143 ns | 0.1069 ns | 24.936 ns | 24.783 ns | 25.110 ns |         - |
| Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | 3079         | 67.412 ns | 0.5054 ns | 0.4221 ns | 67.317 ns | 66.923 ns | 68.465 ns |         - |

dotnet-policy-service · 2024-12-02T22:05:01Z

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...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

…runtime into lzcnt-avx512-vbmi

stephentoub

Thanks! Seems reasonable to me, but @tannergooding should have a look, too.

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

…ors/netcore/TensorPrimitives.LeadingZeroCount.cs Co-authored-by: Tanner Gooding <[email protected]>

tannergooding · 2024-12-04T16:28:04Z

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

+                        Vector128<uint> lowHalf = Vector128.Create((uint)0x0000FFFF);
+                        Vector128<uint> x_bot16 = Sse2.Or(Sse2.ShiftLeftLogical(x.AsUInt32(), 16), lowHalf);
+                        Vector128<uint> x_top16 = Sse2.Or(x.AsUInt32(), lowHalf);
+                        Vector128<uint> lz_bot16 = Avx512CD.VL.LeadingZeroCount(x_bot16);
+                        Vector128<uint> lz_top16 = Avx512CD.VL.LeadingZeroCount(x_top16);
+                        Vector128<uint> lz_top16_shift = Sse2.ShiftLeftLogical(lz_top16, 16);
+                        return Sse2.Or(lz_bot16, lz_top16_shift).AsUInt16().As<ushort, T>();


Is this cheaper than:

Vector256<int> x32 = Avx2.ConvertToVector256Int32(x.AsUInt16()); Vector256<int> lz = Avx512CD.VL.LeadingZeroCount(x32); return Avx512F.VL.ConvertToVector128UInt16(lz) - Vector128.Create(16);

Widening-unwidening has slightly worse performance here:

| Type | Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |------------------------------------------ |----------------- |----------- |------------------------------------------------ |------------- |----------:|----------:|----------:|----------:|----------:|----------:|------:|--------:|----------:|------------:| | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-WWKLJQ | Current PR | 8 | 2.499 ns | 0.0319 ns | 0.0299 ns | 2.505 ns | 2.401 ns | 2.528 ns | 1.00 | 0.02 | - | NA | | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-VFJFZO | Widen | 8 | 2.633 ns | 0.0478 ns | 0.0447 ns | 2.650 ns | 2.570 ns | 2.676 ns | 1.05 | 0.02 | - | NA |

This comment (and the related ones below) are still pending a response.

The current pattern used looks significantly more expensive (in size, instruction count, and micro-ops) than the more naive method of widen + lzcnt + narrow + subtract

The current pattern used looks significantly more expensive (in size, instruction count, and micro-ops) than the more naive method of widen + lzcnt + narrow + subtract

I agree that it does look more expensive because of the increased instruction count, however the benchmark shows that the current pattern is more performant because it avoids the overhead of widening+narrowing.

Another example, this one using BufferSize=3079 like in the original benchmark:

| Type | Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |------------------------------------------ |----------------- |----------- |----------------------------------------------------------------------------------------------- |------------- |--------------:|------------:|------------:|--------------:|--------------:|--------------:|------:|--------:|----------:|------------:| | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-QIBRWQ | Current PR | 3079 | 66.700 ns | 0.3187 ns | 0.2981 ns | 66.761 ns | 66.319 ns | 67.168 ns | 1.00 | 0.01 | - | NA | | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-TEWLEG | Widen + LZCNT + Subtract + Narrow | 3079 | 133.049 ns | 2.4410 ns | 2.1639 ns | 132.240 ns | 131.039 ns | 138.318 ns | 1.99 | 0.03 | - | NA |

And looking at the codegen for this case, the current PR actually generates a smaller function than the widen+narrow suggestion.

This function is where the hot loop of the benchmark is. Current PR generates a function with 1915 bytes of code, Widen+Narrow generates a function with 2356 bytes of code:

Codegen - Current PR

; Assembly listing for method System.Numerics.Tensors.TensorPrimitives:<InvokeSpanIntoSpan>g__Vectorized512|105_3[short,short,System.Numerics.Tensors.TensorPrimitives+LeadingZeroCountOperator`1[short]](byref,byref,ulong) (FullOpts) ; Emitting BLENDED_CODE for X64 with AVX512 - Windows ; FullOpts code ; optimized code ; rsp based frame ; fully interruptible ; No PGO data ; 0 inlinees with PGO data; 24 single block inlinees; 25 inlinees without PGO data G_M000_IG01: ;; offset=0x0000 push rbx sub rsp, 16 xor eax, eax mov qword ptr [rsp+0x08], rax mov qword ptr [rsp], rax G_M000_IG02: ;; offset=0x0010 mov rax, rdx vmovups zmm0, zmmword ptr [rcx] vmovups zmm1, zmmword ptr [reloc @RWD00] vmovaps zmm2, zmm1 vpslld zmm3, zmm0, 16 vpord zmm3, zmm3, zmm2 vplzcntd zmm3, zmm3 vpord zmm0, zmm0, zmm2 vplzcntd zmm0, zmm0 vpslld zmm0, zmm0, 16 vpord zmm0, zmm0, zmm3 vmovups zmm2, zmmword ptr [rcx+2*r8-0x40] vmovaps zmm3, zmm1 vpslld zmm4, zmm2, 16 vpord zmm4, zmm4, zmm3 vplzcntd zmm4, zmm4 vpord zmm2, zmm2, zmm3 vplzcntd zmm2, zmm2 vpslld zmm2, zmm2, 16 vpord zmm2, zmm2, zmm4 cmp r8, 256 jbe G_M000_IG13 G_M000_IG03: ;; offset=0x009C mov bword ptr [rsp+0x08], rcx mov bword ptr [rsp], rax mov rdx, rcx mov r10, rax mov r9, r10 test r9b, 1 sete r11b movzx r11, r11b test r11d, r11d je SHORT G_M000_IG04 mov rdx, r10 and rdx, 63 neg rdx add rdx, 64 shr rdx, 1 lea r9, [rdx+rdx] add rcx, r9 add r9, r10 sub r8, rdx mov rdx, rcx G_M000_IG04: ;; offset=0x00E0 cmp r8, 0x20000 seta cl movzx rcx, cl test ecx, r11d je G_M000_IG07 jmp G_M000_IG10 G_M000_IG05: ;; offset=0x00FB vmovups zmm3, zmmword ptr [rdx] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmm4, zmmword ptr [rdx+0x40] vmovaps zmm5, zmm1 vpslld zmm16, zmm4, 16 vpord zmm16, zmm16, zmm5 vplzcntd zmm16, zmm16 vpord zmm4, zmm4, zmm5 vplzcntd zmm4, zmm4 vpslld zmm4, zmm4, 16 vpord zmm4, zmm4, zmm16 vmovups zmm5, zmmword ptr [rdx+0x80] vmovaps zmm16, zmm1 vpslld zmm17, zmm5, 16 vpord zmm17, zmm17, zmm16 vplzcntd zmm17, zmm17 vpord zmm5, zmm5, zmm16 vplzcntd zmm5, zmm5 vpslld zmm5, zmm5, 16 vpord zmm5, zmm5, zmm17 vmovups zmm16, zmmword ptr [rdx+0xC0] vmovaps zmm17, zmm1 vpslld zmm18, zmm16, 16 vpord zmm18, zmm18, zmm17 vplzcntd zmm18, zmm18 vpord zmm16, zmm16, zmm17 vplzcntd zmm16, zmm16 vpslld zmm16, zmm16, 16 vpord zmm16, zmm16, zmm18 vmovups zmmword ptr [r9], zmm3 vmovups zmmword ptr [r9+0x40], zmm4 vmovups zmmword ptr [r9+0x80], zmm5 vmovups zmmword ptr [r9+0xC0], zmm16 vmovups zmm3, zmmword ptr [rdx+0x100] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmm4, zmmword ptr [rdx+0x140] vmovaps zmm5, zmm1 vpslld zmm16, zmm4, 16 vpord zmm16, zmm16, zmm5 vplzcntd zmm16, zmm16 vpord zmm4, zmm4, zmm5 vplzcntd zmm4, zmm4 vpslld zmm4, zmm4, 16 vpord zmm4, zmm4, zmm16 vmovups zmm5, zmmword ptr [rdx+0x180] vmovaps zmm16, zmm1 vpslld zmm17, zmm5, 16 vpord zmm17, zmm17, zmm16 vplzcntd zmm17, zmm17 vpord zmm5, zmm5, zmm16 vplzcntd zmm5, zmm5 vpslld zmm5, zmm5, 16 vpord zmm5, zmm5, zmm17 vmovups zmm16, zmmword ptr [rdx+0x1C0] vmovaps zmm17, zmm1 vpslld zmm18, zmm16, 16 vpord zmm18, zmm18, zmm17 G_M000_IG06: ;; offset=0x02BE vplzcntd zmm18, zmm18 vpord zmm16, zmm16, zmm17 vplzcntd zmm16, zmm16 vpslld zmm16, zmm16, 16 vpord zmm16, zmm16, zmm18 vmovups zmmword ptr [r9+0x100], zmm3 vmovups zmmword ptr [r9+0x140], zmm4 vmovups zmmword ptr [r9+0x180], zmm5 vmovups zmmword ptr [r9+0x1C0], zmm16 add rdx, 512 add r9, 512 add r8, -256 G_M000_IG07: ;; offset=0x030E cmp r8, 256 jae G_M000_IG05 jmp G_M000_IG11 align [0 bytes for IG08] G_M000_IG08: ;; offset=0x0320 vmovups zmm3, zmmword ptr [rdx] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmm4, zmmword ptr [rdx+0x40] vmovaps zmm5, zmm1 vpslld zmm16, zmm4, 16 vpord zmm16, zmm16, zmm5 vplzcntd zmm16, zmm16 vpord zmm4, zmm4, zmm5 vplzcntd zmm4, zmm4 vpslld zmm4, zmm4, 16 vpord zmm4, zmm4, zmm16 vmovups zmm5, zmmword ptr [rdx+0x80] vmovaps zmm16, zmm1 vpslld zmm17, zmm5, 16 vpord zmm17, zmm17, zmm16 vplzcntd zmm17, zmm17 vpord zmm5, zmm5, zmm16 vplzcntd zmm5, zmm5 vpslld zmm5, zmm5, 16 vpord zmm5, zmm5, zmm17 vmovups zmm16, zmmword ptr [rdx+0xC0] vmovaps zmm17, zmm1 vpslld zmm18, zmm16, 16 vpord zmm18, zmm18, zmm17 vplzcntd zmm18, zmm18 vpord zmm16, zmm16, zmm17 vplzcntd zmm16, zmm16 vpslld zmm16, zmm16, 16 vpord zmm16, zmm16, zmm18 vmovntdq zmmword ptr [r9], zmm3 vmovntdq zmmword ptr [r9+0x40], zmm4 vmovntdq zmmword ptr [r9+0x80], zmm5 vmovntdq zmmword ptr [r9+0xC0], zmm16 vmovups zmm3, zmmword ptr [rdx+0x100] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmm4, zmmword ptr [rdx+0x140] vmovaps zmm5, zmm1 vpslld zmm16, zmm4, 16 vpord zmm16, zmm16, zmm5 vplzcntd zmm16, zmm16 vpord zmm4, zmm4, zmm5 vplzcntd zmm4, zmm4 vpslld zmm4, zmm4, 16 vpord zmm4, zmm4, zmm16 vmovups zmm5, zmmword ptr [rdx+0x180] vmovaps zmm16, zmm1 vpslld zmm17, zmm5, 16 vpord zmm17, zmm17, zmm16 vplzcntd zmm17, zmm17 vpord zmm5, zmm5, zmm16 vplzcntd zmm5, zmm5 vpslld zmm5, zmm5, 16 vpord zmm5, zmm5, zmm17 vmovups zmm16, zmmword ptr [rdx+0x1C0] vmovaps zmm17, zmm1 vpslld zmm18, zmm16, 16 vpord zmm18, zmm18, zmm17 G_M000_IG09: ;; offset=0x04E3 vplzcntd zmm18, zmm18 vpord zmm16, zmm16, zmm17 vplzcntd zmm16, zmm16 vpslld zmm16, zmm16, 16 vpord zmm16, zmm16, zmm18 vmovntdq zmmword ptr [r9+0x100], zmm3 vmovntdq zmmword ptr [r9+0x140], zmm4 vmovntdq zmmword ptr [r9+0x180], zmm5 vmovntdq zmmword ptr [r9+0x1C0], zmm16 add rdx, 512 add r9, 512 add r8, -256 G_M000_IG10: ;; offset=0x0533 cmp r8, 256 jae G_M000_IG08 G_M000_IG11: ;; offset=0x0540 mov rcx, rdx mov rdx, r9 xor r10d, r10d mov bword ptr [rsp], r10 G_M000_IG12: ;; offset=0x054D mov bword ptr [rsp+0x08], r10 G_M000_IG13: ;; offset=0x0552 mov r10, r8 lea r8, [r10+0x1F] and r8, -32 mov r9, r8 shr r9, 5 cmp r9, 8 ja G_M000_IG26 G_M000_IG14: ;; offset=0x056E cmp r9d, 8 ja G_M000_IG26 G_M000_IG15: ;; offset=0x0578 mov r9d, r9d lea r11, [reloc @RWD64] mov r11d, dword ptr [r11+4*r9] lea rbx, G_M000_IG02 add r11, rbx jmp r11 G_M000_IG16: ;; offset=0x0593 vmovups zmm3, zmmword ptr [rcx+2*r8-0x200] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmmword ptr [rdx+2*r8-0x200], zmm3 G_M000_IG17: ;; offset=0x05D5 vmovups zmm3, zmmword ptr [rcx+2*r8-0x1C0] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmmword ptr [rdx+2*r8-0x1C0], zmm3 G_M000_IG18: ;; offset=0x0617 vmovups zmm3, zmmword ptr [rcx+2*r8-0x180] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmmword ptr [rdx+2*r8-0x180], zmm3 G_M000_IG19: ;; offset=0x0659 vmovups zmm3, zmmword ptr [rcx+2*r8-0x140] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmmword ptr [rdx+2*r8-0x140], zmm3 G_M000_IG20: ;; offset=0x069B vmovups zmm3, zmmword ptr [rcx+2*r8-0x100] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmmword ptr [rdx+2*r8-0x100], zmm3 G_M000_IG21: ;; offset=0x06DD vmovups zmm3, zmmword ptr [rcx+2*r8-0xC0] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5 vmovups zmmword ptr [rdx+2*r8-0xC0], zmm3 G_M000_IG22: ;; offset=0x071F vmovups zmm3, zmmword ptr [rcx+2*r8-0x80] vpslld zmm4, zmm3, 16 vpord zmm4, zmm4, zmm1 vplzcntd zmm4, zmm4 vpord zmm1, zmm3, zmm1 vplzcntd zmm1, zmm1 vpslld zmm1, zmm1, 16 vpord zmm1, zmm1, zmm4 vmovups zmmword ptr [rdx+2*r8-0x80], zmm1 G_M000_IG23: ;; offset=0x075B vmovups zmmword ptr [rdx+2*r10-0x40], zmm2 G_M000_IG24: ;; offset=0x0763 vmovups zmmword ptr [rax], zmm0 G_M000_IG25: ;; offset=0x0769 vzeroupper add rsp, 16 pop rbx ret G_M000_IG26: ;; offset=0x0772 vzeroupper add rsp, 16 pop rbx ret RWD00 dq 0000FFFF0000FFFFh, 0000FFFF0000FFFFh, 0000FFFF0000FFFFh, 0000FFFF0000FFFFh, 0000FFFF0000FFFFh, 0000FFFF0000FFFFh, 0000FFFF0000FFFFh, 0000FFFF0000FFFFh RWD64 dd 00000753h ; case G_M000_IG24 dd 0000074Bh ; case G_M000_IG23 dd 0000070Fh ; case G_M000_IG22 dd 000006CDh ; case G_M000_IG21 dd 0000068Bh ; case G_M000_IG20 dd 00000649h ; case G_M000_IG19 dd 00000607h ; case G_M000_IG18 dd 000005C5h ; case G_M000_IG17 dd 00000583h ; case G_M000_IG16 ; Total bytes of code 1915

Codegen - Widen+narrow

; Assembly listing for method System.Numerics.Tensors.TensorPrimitives:<InvokeSpanIntoSpan>g__Vectorized512|105_3[short,short,System.Numerics.Tensors.TensorPrimitives+LeadingZeroCountOperator`1[short]](byref,byref,ulong) (FullOpts) ; Emitting BLENDED_CODE for X64 with AVX512 - Windows ; FullOpts code ; optimized code ; rsp based frame ; fully interruptible ; No PGO data ; 0 inlinees with PGO data; 124 single block inlinees; 50 inlinees without PGO data G_M000_IG01: ;; offset=0x0000 push rbx sub rsp, 16 xor eax, eax mov qword ptr [rsp+0x08], rax mov qword ptr [rsp], rax vxorps xmm0, xmm0, xmm0 vmovaps xmm1, xmm0 vmovaps xmm2, xmm0 vmovaps xmm3, xmm0 vmovaps xmm4, xmm0 vmovaps xmm5, xmm0 vmovaps xmm16, xmm0 vmovaps xmm17, xmm0 vmovaps xmm18, xmm0 vmovaps xmm19, xmm0 vmovaps xmm20, xmm0 vmovaps xmm21, xmm0 vmovaps xmm22, xmm0 vmovaps xmm23, xmm0 vmovaps xmm24, xmm0 vmovaps xmm25, xmm0 G_M000_IG02: ;; offset=0x0064 mov rax, rdx vmovups zmm26, zmmword ptr [rcx] vextracti32x8 ymm27, zmm26, 1 vpmovzxwd zmm27, zmm27 vplzcntd zmm27, zmm27 vpmovzxwd zmm26, zmm26 vplzcntd zmm26, zmm26 vpmovdw zmm26, zmm26 vpmovdw zmm27, zmm27 vxorps ymm28, ymm28, ymm28 vinsertf64x4 zmm26, zmm28, ymm26, 0 vinsertf64x4 zmm26, zmm26, ymm27, 1 vmovups zmm27, zmmword ptr [reloc @RWD00] vpsubw zmm26, zmm26, zmm27 vmovups zmm28, zmmword ptr [rcx+2*r8-0x40] vextracti32x8 ymm29, zmm28, 1 vpmovzxwd zmm29, zmm29 vplzcntd zmm29, zmm29 vpmovzxwd zmm28, zmm28 vplzcntd zmm28, zmm28 vpmovdw zmm28, zmm28 vpmovdw zmm29, zmm29 vxorps ymm30, ymm30, ymm30 vinsertf64x4 zmm28, zmm30, ymm28, 0 vinsertf64x4 zmm28, zmm28, ymm29, 1 vpsubw zmm28, zmm28, zmm27 cmp r8, 256 jbe G_M000_IG13 G_M000_IG03: ;; offset=0x0116 mov bword ptr [rsp+0x08], rcx mov bword ptr [rsp], rax mov rdx, rcx mov r10, rax mov r9, r10 test r9b, 1 sete r11b movzx r11, r11b test r11d, r11d je SHORT G_M000_IG04 mov rdx, r10 and rdx, 63 neg rdx add rdx, 64 shr rdx, 1 lea r9, [rdx+rdx] add rcx, r9 add r9, r10 sub r8, rdx mov rdx, rcx G_M000_IG04: ;; offset=0x015A cmp r8, 0x20000 seta cl movzx rcx, cl test ecx, r11d jne G_M000_IG10 align [0 bytes for IG05] G_M000_IG05: ;; offset=0x0170 cmp r8, 256 jb G_M000_IG11 G_M000_IG06: ;; offset=0x017D vmovups zmm0, zmmword ptr [rdx] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm2, zmm1 vplzcntd zmm3, zmm2 vpmovzxwd zmm4, zmm0 vplzcntd zmm5, zmm4 vpmovdw zmm5, zmm16 vpmovdw zmm3, zmm17 vinsertf64x4 zmm18, zmm18, ymm16, 0 vinsertf64x4 zmm18, zmm18, ymm17, 1 vpsubw zmm0, zmm18, zmm27 vmovups zmm1, zmmword ptr [rdx+0x40] vextracti32x8 ymm2, zmm1, 1 vpmovzxwd zmm2, zmm2 vplzcntd zmm2, zmm2 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovdw zmm1, zmm1 vpmovdw zmm2, zmm2 vinsertf64x4 zmm19, zmm19, ymm1, 0 vinsertf64x4 zmm19, zmm19, ymm2, 1 vpsubw zmm1, zmm19, zmm27 vmovups zmm2, zmmword ptr [rdx+0x80] vextracti32x8 ymm3, zmm2, 1 vpmovzxwd zmm3, zmm3 vplzcntd zmm3, zmm3 vpmovzxwd zmm2, zmm2 vplzcntd zmm2, zmm2 vpmovdw zmm2, zmm2 vpmovdw zmm3, zmm3 vinsertf64x4 zmm20, zmm20, ymm2, 0 vinsertf64x4 zmm20, zmm20, ymm3, 1 vpsubw zmm2, zmm20, zmm27 vmovups zmm3, zmmword ptr [rdx+0xC0] vextracti32x8 ymm4, zmm3, 1 vpmovzxwd zmm4, zmm4 vplzcntd zmm4, zmm4 vpmovzxwd zmm3, zmm3 vplzcntd zmm3, zmm3 vpmovdw zmm3, zmm3 vpmovdw zmm4, zmm4 vinsertf64x4 zmm21, zmm21, ymm3, 0 vinsertf64x4 zmm21, zmm21, ymm4, 1 vpsubw zmm3, zmm21, zmm27 vmovups zmmword ptr [r9], zmm0 vmovups zmmword ptr [r9+0x40], zmm1 vmovups zmmword ptr [r9+0x80], zmm2 vmovups zmmword ptr [r9+0xC0], zmm3 vmovups zmm0, zmmword ptr [rdx+0x100] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm2, zmm1 vplzcntd zmm1, zmm2 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vinsertf64x4 zmm22, zmm22, ymm0, 0 vinsertf64x4 zmm22, zmm22, ymm1, 1 vpsubw zmm0, zmm22, zmm27 vmovups zmm1, zmmword ptr [rdx+0x140] vextracti32x8 ymm2, zmm1, 1 vpmovzxwd zmm2, zmm2 vplzcntd zmm2, zmm2 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovdw zmm1, zmm1 vpmovdw zmm2, zmm2 vinsertf64x4 zmm23, zmm23, ymm1, 0 vinsertf64x4 zmm23, zmm23, ymm2, 1 vpsubw zmm1, zmm23, zmm27 vmovups zmm2, zmmword ptr [rdx+0x180] vextracti32x8 ymm3, zmm2, 1 vpmovzxwd zmm3, zmm3 vplzcntd zmm3, zmm3 G_M000_IG07: ;; offset=0x0355 vpmovzxwd zmm2, zmm2 vplzcntd zmm2, zmm2 vpmovdw zmm2, zmm2 vpmovdw zmm3, zmm3 vinsertf64x4 zmm24, zmm24, ymm2, 0 vinsertf64x4 zmm24, zmm24, ymm3, 1 vpsubw zmm2, zmm24, zmm27 vmovups zmm3, zmmword ptr [rdx+0x1C0] vextracti32x8 ymm4, zmm3, 1 vpmovzxwd zmm4, zmm4 vplzcntd zmm4, zmm4 vpmovzxwd zmm3, zmm3 vplzcntd zmm3, zmm3 vpmovdw zmm3, zmm3 vpmovdw zmm4, zmm4 vinsertf64x4 zmm25, zmm25, ymm3, 0 vinsertf64x4 zmm25, zmm25, ymm4, 1 vpsubw zmm3, zmm25, zmm27 vmovups zmmword ptr [r9+0x100], zmm0 vmovups zmmword ptr [r9+0x140], zmm1 vmovups zmmword ptr [r9+0x180], zmm2 vmovups zmmword ptr [r9+0x1C0], zmm3 add rdx, 512 add r9, 512 add r8, -256 jmp G_M000_IG05 align [0 bytes for IG08] G_M000_IG08: ;; offset=0x03FD vmovups zmm18, zmmword ptr [rdx] vextracti32x8 ymm19, zmm18, 1 vpmovzxwd zmm19, zmm19 vplzcntd zmm19, zmm19 vpmovzxwd zmm18, zmm18 vplzcntd zmm18, zmm18 vpmovdw zmm18, zmm18 vpmovdw zmm19, zmm19 vinsertf64x4 zmm0, zmm0, ymm18, 0 vinsertf64x4 zmm0, zmm0, ymm19, 1 vpsubw zmm18, zmm0, zmm27 vmovups zmm19, zmmword ptr [rdx+0x40] vextracti32x8 ymm20, zmm19, 1 vpmovzxwd zmm20, zmm20 vplzcntd zmm20, zmm20 vpmovzxwd zmm19, zmm19 vplzcntd zmm19, zmm19 vpmovdw zmm19, zmm19 vpmovdw zmm20, zmm20 vinsertf64x4 zmm1, zmm1, ymm19, 0 vinsertf64x4 zmm1, zmm1, ymm20, 1 vpsubw zmm19, zmm1, zmm27 vmovups zmm20, zmmword ptr [rdx+0x80] vextracti32x8 ymm21, zmm20, 1 vpmovzxwd zmm21, zmm21 vplzcntd zmm21, zmm21 vpmovzxwd zmm20, zmm20 vplzcntd zmm20, zmm20 vpmovdw zmm20, zmm20 vpmovdw zmm21, zmm21 vinsertf64x4 zmm2, zmm2, ymm20, 0 vinsertf64x4 zmm2, zmm2, ymm21, 1 vpsubw zmm20, zmm2, zmm27 vmovups zmm21, zmmword ptr [rdx+0xC0] vextracti32x8 ymm22, zmm21, 1 vpmovzxwd zmm22, zmm22 vplzcntd zmm22, zmm22 vpmovzxwd zmm21, zmm21 vplzcntd zmm21, zmm21 vpmovdw zmm21, zmm21 vpmovdw zmm22, zmm22 vinsertf64x4 zmm3, zmm3, ymm21, 0 vinsertf64x4 zmm3, zmm3, ymm22, 1 vpsubw zmm21, zmm3, zmm27 vmovntdq zmmword ptr [r9], zmm18 vmovntdq zmmword ptr [r9+0x40], zmm19 vmovntdq zmmword ptr [r9+0x80], zmm20 vmovntdq zmmword ptr [r9+0xC0], zmm21 vmovups zmm18, zmmword ptr [rdx+0x100] vextracti32x8 ymm19, zmm18, 1 vpmovzxwd zmm20, zmm19 vplzcntd zmm19, zmm20 vpmovzxwd zmm18, zmm18 vplzcntd zmm18, zmm18 vpmovdw zmm18, zmm18 vpmovdw zmm19, zmm19 vinsertf64x4 zmm4, zmm4, ymm18, 0 vinsertf64x4 zmm4, zmm4, ymm19, 1 vpsubw zmm18, zmm4, zmm27 vmovups zmm19, zmmword ptr [rdx+0x140] vextracti32x8 ymm20, zmm19, 1 vpmovzxwd zmm20, zmm20 vplzcntd zmm20, zmm20 vpmovzxwd zmm19, zmm19 vplzcntd zmm19, zmm19 vpmovdw zmm19, zmm19 vpmovdw zmm20, zmm20 vinsertf64x4 zmm5, zmm5, ymm19, 0 vinsertf64x4 zmm5, zmm5, ymm20, 1 vpsubw zmm19, zmm5, zmm27 vmovups zmm20, zmmword ptr [rdx+0x180] vextracti32x8 ymm21, zmm20, 1 vpmovzxwd zmm21, zmm21 vplzcntd zmm21, zmm21 G_M000_IG09: ;; offset=0x05D5 vpmovzxwd zmm20, zmm20 vplzcntd zmm20, zmm20 vpmovdw zmm20, zmm20 vpmovdw zmm21, zmm21 vinsertf64x4 zmm16, zmm16, ymm20, 0 vinsertf64x4 zmm16, zmm16, ymm21, 1 vpsubw zmm20, zmm16, zmm27 vmovups zmm21, zmmword ptr [rdx+0x1C0] vextracti32x8 ymm22, zmm21, 1 vpmovzxwd zmm22, zmm22 vplzcntd zmm22, zmm22 vpmovzxwd zmm21, zmm21 vplzcntd zmm21, zmm21 vpmovdw zmm21, zmm21 vpmovdw zmm22, zmm22 vinsertf64x4 zmm17, zmm17, ymm21, 0 vinsertf64x4 zmm17, zmm17, ymm22, 1 vpsubw zmm21, zmm17, zmm27 vmovntdq zmmword ptr [r9+0x100], zmm18 vmovntdq zmmword ptr [r9+0x140], zmm19 vmovntdq zmmword ptr [r9+0x180], zmm20 vmovntdq zmmword ptr [r9+0x1C0], zmm21 add rdx, 512 add r9, 512 add r8, -256 G_M000_IG10: ;; offset=0x0678 cmp r8, 256 jae G_M000_IG08 G_M000_IG11: ;; offset=0x0685 mov rcx, rdx mov rdx, r9 xor r10d, r10d mov bword ptr [rsp], r10 G_M000_IG12: ;; offset=0x0692 mov bword ptr [rsp+0x08], r10 G_M000_IG13: ;; offset=0x0697 mov r10, r8 lea r8, [r10+0x1F] and r8, -32 mov r9, r8 shr r9, 5 cmp r9, 8 ja G_M000_IG25 G_M000_IG14: ;; offset=0x06B3 cmp r9d, 8 ja G_M000_IG25 G_M000_IG15: ;; offset=0x06BD mov r9d, r9d lea r11, [reloc @RWD64] mov r11d, dword ptr [r11+4*r9] lea rbx, G_M000_IG02 add r11, rbx jmp r11 G_M000_IG16: ;; offset=0x06D8 vmovups zmm0, zmmword ptr [rcx+2*r8-0x200] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0x200], zmm0 G_M000_IG17: ;; offset=0x072B vmovups zmm0, zmmword ptr [rcx+2*r8-0x1C0] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0x1C0], zmm0 G_M000_IG18: ;; offset=0x077E vmovups zmm0, zmmword ptr [rcx+2*r8-0x180] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0x180], zmm0 G_M000_IG19: ;; offset=0x07D1 vmovups zmm0, zmmword ptr [rcx+2*r8-0x140] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0x140], zmm0 G_M000_IG20: ;; offset=0x0824 vmovups zmm0, zmmword ptr [rcx+2*r8-0x100] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0x100], zmm0 G_M000_IG21: ;; offset=0x0877 vmovups zmm0, zmmword ptr [rcx+2*r8-0xC0] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0xC0], zmm0 G_M000_IG22: ;; offset=0x08CA vmovups zmm0, zmmword ptr [rcx+2*r8-0x80] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm1, zmm1 vplzcntd zmm1, zmm1 vpmovzxwd zmm0, zmm0 vplzcntd zmm0, zmm0 vpmovdw zmm0, zmm0 vpmovdw zmm1, zmm1 vxorps ymm2, ymm2, ymm2 vinsertf64x4 zmm0, zmm2, ymm0, 0 vinsertf64x4 zmm0, zmm0, ymm1, 1 vpsubw zmm0, zmm0, zmm27 vmovups zmmword ptr [rdx+2*r8-0x80], zmm0 G_M000_IG23: ;; offset=0x091D vmovups zmmword ptr [rdx+2*r10-0x40], zmm28 G_M000_IG24: ;; offset=0x0925 vmovups zmmword ptr [rax], zmm26 G_M000_IG25: ;; offset=0x092B vzeroupper add rsp, 16 pop rbx ret RWD00 dq 0010001000100010h, 0010001000100010h, 0010001000100010h, 0010001000100010h, 0010001000100010h, 0010001000100010h, 0010001000100010h, 0010001000100010h RWD64 dd 000008C1h ; case G_M000_IG24 dd 000008B9h ; case G_M000_IG23 dd 00000866h ; case G_M000_IG22 dd 00000813h ; case G_M000_IG21 dd 000007C0h ; case G_M000_IG20 dd 0000076Dh ; case G_M000_IG19 dd 0000071Ah ; case G_M000_IG18 dd 000006C7h ; case G_M000_IG17 dd 00000674h ; case G_M000_IG16 ; Total bytes of code 2356

One more thing to highlight in the above codegen is that the current PR produces 9 instructions for each invocation of LeadingZeroCount. Using the widen+narrow approach produces 11 instructions.

Original loop is unrolled, I'm copying/pasting the codegen of just one invocation of LeadingZeroCount:

Current PR -- 9 total instructions

vmovups zmm3, zmmword ptr [rdx] vmovaps zmm4, zmm1 vpslld zmm5, zmm3, 16 vpord zmm5, zmm5, zmm4 vplzcntd zmm5, zmm5 vpord zmm3, zmm3, zmm4 vplzcntd zmm3, zmm3 vpslld zmm3, zmm3, 16 vpord zmm3, zmm3, zmm5

Widen+Narrow -- 11 total instructions

vmovups zmm0, zmmword ptr [rdx] vextracti32x8 ymm1, zmm0, 1 vpmovzxwd zmm2, zmm1 vplzcntd zmm3, zmm2 vpmovzxwd zmm4, zmm0 vplzcntd zmm5, zmm4 vpmovdw zmm5, zmm16 vpmovdw zmm3, zmm17 vinsertf64x4 zmm18, zmm18, ymm16, 0 vinsertf64x4 zmm18, zmm18, ymm17, 1 vpsubw zmm0, zmm18, zmm27

I'm still proposing the current PR as it produces faster and smaller codegen.

tannergooding · 2024-12-04T16:28:53Z

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

+                    Vector128<byte> lookupVectorLow = Vector128.Create((byte)8, 7, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4);
+                    Vector128<byte> lookupVectorHigh = Vector128.Create((byte)3, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0);
+                    Vector128<byte> nibbleMask = Vector128.Create<byte>(0xF);
+                    Vector128<byte> permuteMask = Vector128.Create<byte>(0x80);
+                    Vector128<byte> lowNibble = x.AsByte() & nibbleMask;
+                    Vector128<byte> highNibble = Sse2.ShiftRightLogical(x.AsInt32(), 4).AsByte() & nibbleMask;
+                    Vector128<byte> nibbleSelectMask = Sse2.CompareEqual(highNibble, Vector128<byte>.Zero);
+                    Vector128<byte> indexVector = Sse41.BlendVariable(highNibble, lowNibble, nibbleSelectMask) +
+                        (~nibbleSelectMask & nibbleMask);
+                    indexVector |= ~nibbleSelectMask & permuteMask;
+                    return Avx512Vbmi.VL.PermuteVar16x8x2(lookupVectorLow, indexVector, lookupVectorHigh).As<byte, T>();


Is this cheaper than:

Vector512<int> x32 = Avx512F.ConvertToVector512Int32(x.AsByte()); Vector512<int> lz = Avx512CD.LeadingZeroCount(x32); return Avx512F.ConvertToVector128Byte(lz) - Vector128.Create(24);

There is overhead when widening-unwidening.

For this case, the widening here gives a bimodal performance result. To verify, the same microbenchmark can be modified to stress this path specifically by using BufferLength=16.

Some runs look like this:

| Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |----------------- |----------- |------------------------------------------------ |------------- |----------:|----------:|----------:|----------:|----------:|----------:|------:|--------:|----------:|------------:| | LeadingZeroCount | Job-RJAKMA | Current PR | 16 | 2.676 ns | 0.0525 ns | 0.0491 ns | 2.680 ns | 2.610 ns | 2.754 ns | 1.00 | 0.03 | - | NA | | LeadingZeroCount | Job-FSPMRZ | Widen | 16 | 3.485 ns | 0.0365 ns | 0.0342 ns | 3.502 ns | 3.428 ns | 3.526 ns | 1.30 | 0.03 | - | NA |

Other runs look like this:

| Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |----------------- |----------- |------------------------------------------------ |------------- |----------:|----------:|----------:|----------:|----------:|----------:|------:|--------:|----------:|------------:| | LeadingZeroCount | Job-MGUUAK | Current PR | 16 | 2.683 ns | 0.0424 ns | 0.0396 ns | 2.695 ns | 2.616 ns | 2.733 ns | 1.00 | 0.02 | - | NA | | LeadingZeroCount | Job-NBPOWJ | Widen | 16 | 2.484 ns | 0.0334 ns | 0.0296 ns | 2.492 ns | 2.427 ns | 2.519 ns | 0.93 | 0.02 | - | NA |

I chose this version because it was more consistent.

tannergooding · 2024-12-04T16:30:26Z

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

+                    Vector256<byte> lookupVector =
+                        Vector256.Create((byte)8, 7, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4,
+                                               3, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0);
+                    Vector256<byte> nibbleMask = Vector256.Create<byte>(0xF);
+                    Vector256<byte> lowNibble = x.AsByte() & nibbleMask;
+                    Vector256<byte> highNibble = Avx2.ShiftRightLogical(x.AsInt32(), 4).AsByte() & nibbleMask;
+                    Vector256<byte> nibbleSelectMask = Avx2.CompareEqual(highNibble, Vector256<byte>.Zero);
+                    Vector256<byte> indexVector = Avx2.BlendVariable(highNibble, lowNibble, nibbleSelectMask) +
+                        (~nibbleSelectMask & nibbleMask);
+                    return Avx512Vbmi.VL.PermuteVar32x8(lookupVector, indexVector).As<byte, T>();


Similar question as previous, but doing WidenLower/WidenUpper since its 1024 bits total.

Similar comment as the Vector128<byte> case.

There is overhead when widening-unwidening. It isn't as bad here, but both versions perform very similarly. Can be verified with BufferLength=32 to stress this path:

| Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |----------------- |----------- |------------------------------------------------ |------------- |----------:|----------:|----------:|----------:|----------:|----------:|------:|--------:|----------:|------------:| | LeadingZeroCount | Job-RJAKMA | Current PR | 32 | 2.682 ns | 0.0387 ns | 0.0362 ns | 2.702 ns | 2.614 ns | 2.724 ns | 1.00 | 0.02 | - | NA | | LeadingZeroCount | Job-FSPMRZ | Widen | 32 | 2.685 ns | 0.0312 ns | 0.0292 ns | 2.691 ns | 2.608 ns | 2.721 ns | 0.91 | 0.02 | - | NA |

tannergooding · 2024-12-04T16:30:38Z

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

+                        Vector256<uint> lowHalf = Vector256.Create((uint)0x0000FFFF);
+                        Vector256<uint> x_bot16 = Avx2.Or(Avx2.ShiftLeftLogical(x.AsUInt32(), 16), lowHalf);
+                        Vector256<uint> x_top16 = Avx2.Or(x.AsUInt32(), lowHalf);
+                        Vector256<uint> lz_bot16 = Avx512CD.VL.LeadingZeroCount(x_bot16);
+                        Vector256<uint> lz_top16 = Avx512CD.VL.LeadingZeroCount(x_top16);
+                        Vector256<uint> lz_top16_shift = Avx2.ShiftLeftLogical(lz_top16, 16);
+                        return Avx2.Or(lz_bot16, lz_top16_shift).AsUInt16().As<ushort, T>();


Similar question as previous, widening to Vector512

Widening-unwidening has similar performance in this case. Can be verified with BufferLength=16:

| Type | Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |------------------------------------------ |----------------- |----------- |------------------------------------------------ |------------- |----------:|----------:|----------:|----------:|----------:|----------:|------:|--------:|----------:|------------:| | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-WWKLJQ | Current PR | 16 | 2.485 ns | 0.0442 ns | 0.0414 ns | 2.496 ns | 2.410 ns | 2.530 ns | 1.00 | 0.02 | - | NA | | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-VFJFZO | Widen | 16 | 2.474 ns | 0.0542 ns | 0.0507 ns | 2.495 ns | 2.402 ns | 2.529 ns | 1.00 | 0.03 | - | NA |

tannergooding · 2024-12-04T16:31:26Z

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

+                        Vector512<uint> lowHalf = Vector512.Create((uint)0x0000FFFF);
+                        Vector512<uint> x_bot16 = Avx512F.Or(Avx512F.ShiftLeftLogical(x.AsUInt32(), 16), lowHalf);
+                        Vector512<uint> x_top16 = Avx512F.Or(x.AsUInt32(), lowHalf);
+                        Vector512<uint> lz_bot16 = Avx512CD.LeadingZeroCount(x_bot16);
+                        Vector512<uint> lz_top16 = Avx512CD.LeadingZeroCount(x_top16);
+                        Vector512<uint> lz_top16_shift = Avx512F.ShiftLeftLogical(lz_top16, 16);
+                        return Avx512F.Or(lz_bot16, lz_top16_shift).AsUInt16().As<ushort, T>();


Same question, doing WidenLower/WidenUpper

The widening-unwidening performance difference is most obvious for this case.

| Type | Method | Job | Toolchain | BufferLength | Mean | Error | StdDev | Median | Min | Max | Ratio | RatioSD | Allocated | Alloc Ratio | |------------------------------------------ |----------------- |----------- |------------------------------------------------ |------------- |----------:|----------:|----------:|----------:|----------:|----------:|------:|--------:|----------:|------------:| | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-WWKLJQ | Current PR | 32 | 4.110 ns | 0.0621 ns | 0.0581 ns | 4.141 ns | 4.011 ns | 4.161 ns | 1.00 | 0.02 | - | NA | | Perf_BinaryIntegerTensorPrimitives<Int16> | LeadingZeroCount | Job-VFJFZO | Widen | 32 | 10.421 ns | 0.0738 ns | 0.0690 ns | 10.445 ns | 10.261 ns | 10.515 ns | 2.54 | 0.04 | - | NA |

tannergooding · 2024-12-04T16:37:58Z

...em.Numerics.Tensors/src/System/Numerics/Tensors/netcore/TensorPrimitives.LeadingZeroCount.cs

+                    Vector512<byte> lookupVectorA =
+                        Vector512.Create((byte)8, 7, 6, 6, 5, 5, 5, 5,
+                                               4, 4, 4, 4, 4, 4, 4, 4,
+                                               3, 3, 3, 3, 3, 3, 3, 3,
+                                               3, 3, 3, 3, 3, 3, 3, 3,
+                                               2, 2, 2, 2, 2, 2, 2, 2,
+                                               2, 2, 2, 2, 2, 2, 2, 2,
+                                               2, 2, 2, 2, 2, 2, 2, 2,
+                                               2, 2, 2, 2, 2, 2, 2, 2);
+                    Vector512<byte> lookupVectorB = Vector512.Create((byte)1);
+                    Vector512<byte> bit7ZeroMask = Avx512BW.CompareLessThan(x.AsByte(), Vector512.Create((byte)128));
+                    return Avx512F.And(bit7ZeroMask, Avx512Vbmi.PermuteVar64x8x2(lookupVectorA, x.AsByte(), lookupVectorB)).As<byte, T>();


I think this is the only one that shouldn't be simply Widen+Lzcnt. But it does warrant a comment elaborating on how the lookup works.

In particular, PermuteVar64x8x2 isn't immediately obvious how it operates, so elaborating that x is being used as an index where bit 6 selects the table, bits 5:0 select an index in the table, and anything where bit 7 is set is zeroed is goodness.

Makes sense. I've added a comment to better explain how x is being used as an index and how the intrinsic is choosing between the two lookup vectors.

Alex Covington (Advanced Micro Devices added 3 commits December 2, 2024 13:41

Working for byte

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Add vector support to System.Numerics.Tensors.TensorPrimitives.LeadingZeroCount for Byte and Int16 #110333

Add vector support to System.Numerics.Tensors.TensorPrimitives.LeadingZeroCount for Byte and Int16 #110333

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Add vector support to System.Numerics.Tensors.TensorPrimitives.LeadingZeroCount for Byte and Int16 #110333

Add vector support to System.Numerics.Tensors.TensorPrimitives.LeadingZeroCount for Byte and Int16 #110333

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