mirror of
https://github.com/GOSTSec/ccminer
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lyra2v2: improve speed on SM 5.2 (Cuda 6.5) with sp unrolls
Reduce a bit the 750Ti speed but improve a lot the 9xx speed.
Keep compat for SM 3/3.5 in a second file..
Note: With this code and Cuda 7.5, the speed won is the reverse...
May be "reverted" soon
This commit is contained in:
Tanguy Pruvot
parent
2ebcd1fbd5
commit
b3adebdf2a
@ -334,6 +334,7 @@
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<ClInclude Include="uint256.h" />
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<ClInclude Include="lyra2\Lyra2.h" />
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<ClInclude Include="lyra2\Sponge.h" />
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<ClInclude Include="lyra2\cuda_lyra2v2_sm3.cuh" />
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<ClInclude Include="quark\groestl_transf_quad.h" />
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<ClInclude Include="quark\groestl_functions_quad.h" />
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</ItemGroup>
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@ -610,6 +610,9 @@
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<CudaCompile Include="lyra2\lyra2REv2.cu">
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<Filter>Source Files\CUDA</Filter>
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</CudaCompile>
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<ClInclude Include="lyra2\cuda_lyra2v2_sm3.cuh">
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<Filter>Source Files\CUDA</Filter>
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</ClInclude>
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<CudaCompile Include="zr5.cu">
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<Filter>Source Files\CUDA</Filter>
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</CudaCompile>
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@ -674,4 +677,4 @@
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<Filter>Ressources</Filter>
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</Text>
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</ItemGroup>
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</Project>
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</Project>
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@ -6,66 +6,51 @@
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#define __CUDA_ARCH__ 500
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#endif
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#define TPB52 10
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#define TPB50 16
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#include "cuda_lyra2_vectors.h"
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#define TPB 16
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#include "cuda_lyra2v2_sm3.cuh"
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#ifndef __CUDA_ARCH__
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__device__ void *DMatrix;
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#endif
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#if __CUDA_ARCH__ >= 500
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#define Nrow 4
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#define Ncol 4
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#if __CUDA_ARCH__ < 500
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#define vectype ulonglong4
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#define u64type uint64_t
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#define memshift 4
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#elif __CUDA_ARCH__ == 500
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#define u64type uint2
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#define vectype uint28
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#define memshift 3
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#else
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#define u64type uint2
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#define vectype uint28
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#define memshift 3
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#endif
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__device__ vectype *DMatrix;
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#if __CUDA_ARCH__ >= 300
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#if __CUDA_ARCH__ >= 500
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static __device__ __forceinline__
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void Gfunc_v35(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
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__device__ __forceinline__
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void Gfunc_v5(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
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{
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a += b; d ^= a; d = SWAPUINT2(d);
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c += d; b ^= c; b = ROR24(b);
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a += b; d ^= a; d = ROR16(d);
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c += d; b ^= c; b = ROR2(b, 63);
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}
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#else
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static __device__ __forceinline__
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void Gfunc_v35(unsigned long long &a, unsigned long long &b, unsigned long long &c, unsigned long long &d)
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{
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a += b; d ^= a; d = ROTR64(d, 32);
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c += d; b ^= c; b = ROTR64(b, 24);
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a += b; d ^= a; d = ROTR64(d, 16);
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c += d; b ^= c; b = ROTR64(b, 63);
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}
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#endif
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static __device__ __forceinline__
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void round_lyra_v35(vectype* s)
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__device__ __forceinline__
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void round_lyra_v5(vectype* s)
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{
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Gfunc_v35(s[0].x, s[1].x, s[2].x, s[3].x);
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Gfunc_v35(s[0].y, s[1].y, s[2].y, s[3].y);
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Gfunc_v35(s[0].z, s[1].z, s[2].z, s[3].z);
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Gfunc_v35(s[0].w, s[1].w, s[2].w, s[3].w);
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Gfunc_v5(s[0].x, s[1].x, s[2].x, s[3].x);
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Gfunc_v5(s[0].y, s[1].y, s[2].y, s[3].y);
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Gfunc_v5(s[0].z, s[1].z, s[2].z, s[3].z);
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Gfunc_v5(s[0].w, s[1].w, s[2].w, s[3].w);
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Gfunc_v35(s[0].x, s[1].y, s[2].z, s[3].w);
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Gfunc_v35(s[0].y, s[1].z, s[2].w, s[3].x);
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Gfunc_v35(s[0].z, s[1].w, s[2].x, s[3].y);
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Gfunc_v35(s[0].w, s[1].x, s[2].y, s[3].z);
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Gfunc_v5(s[0].x, s[1].y, s[2].z, s[3].w);
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Gfunc_v5(s[0].y, s[1].z, s[2].w, s[3].x);
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Gfunc_v5(s[0].z, s[1].w, s[2].x, s[3].y);
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Gfunc_v5(s[0].w, s[1].x, s[2].y, s[3].z);
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}
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static __device__ __forceinline__
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__device__ __forceinline__
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void reduceDuplex(vectype state[4], uint32_t thread)
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{
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vectype state1[3];
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@ -82,360 +67,217 @@ void reduceDuplex(vectype state[4], uint32_t thread)
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix+s1)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state[j] ^= state1[j];
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round_lyra_v35(state);
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round_lyra_v5(state);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] ^= state[j];
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#pragma unroll
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state1[j];
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}
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}
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static __device__ __forceinline__
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void reduceDuplexV3(vectype state[4], uint32_t thread)
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__device__ __forceinline__
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void reduceDuplex50(vectype state[4], uint32_t thread)
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{
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vectype state1[3];
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uint32_t ps1 = (Nrow * Ncol * memshift * thread);
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uint32_t ps2 = (memshift * (Ncol - 1) * Nrow + memshift * 1 + Nrow * Ncol * memshift * thread);
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uint32_t ps2 = (memshift * (Ncol - 1) + memshift * Ncol + Nrow * Ncol * memshift * thread);
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#pragma unroll 4
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for (int i = 0; i < Ncol; i++)
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{
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uint32_t s1 = ps1 + Nrow * i *memshift;
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uint32_t s2 = ps2 - Nrow * i *memshift;
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uint32_t s1 = ps1 + i*memshift;
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uint32_t s2 = ps2 - i*memshift;
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix + s1)[j]);
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state[j] ^= __ldg4(&(DMatrix + s1)[j]);
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for (int j = 0; j < 3; j++)
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state[j] ^= state1[j];
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round_lyra_v35(state);
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round_lyra_v5(state);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] ^= state[j];
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state1[j];
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(DMatrix + s2)[j] = __ldg4(&(DMatrix + s1)[j]) ^ state[j];
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}
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}
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static __device__ __forceinline__
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__device__ __forceinline__
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void reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
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{
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vectype state2[3],state1[3];
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vectype state2[3], state1[3];
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uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread);
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uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread);
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uint32_t ps3 = (memshift * (Ncol-1) + memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread);
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//#pragma unroll 1
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for (int i = 0; i < Ncol; i++)
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{
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uint32_t s1 = ps1 + i*memshift;
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uint32_t s2 = ps2 + i*memshift;
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uint32_t s3 = ps3 - i*memshift;
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for (int j = 0; j < 3; j++)
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state1[j]= __ldg4(&(DMatrix + s1)[j]);
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for (int j = 0; j < 3; j++)
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state2[j]= __ldg4(&(DMatrix + s2)[j]);
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for (int j = 0; j < 3; j++) {
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vectype tmp = state1[j] + state2[j];
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state[j] ^= tmp;
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}
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#if __CUDA_ARCH__ == 500
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round_lyra_v35(state);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state[j] = state[j] ^ (__ldg4(&(DMatrix + s1)[j]) + __ldg4(&(DMatrix + s2)[j]));
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for (int j = 0; j < 3; j++) {
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round_lyra_v5(state);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix + s1)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state2[j] = __ldg4(&(DMatrix + s2)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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{
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state1[j] ^= state[j];
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(DMatrix + s3)[j] = state1[j];
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}
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#else /* 5.2 */
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix + s1)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state2[j] = __ldg4(&(DMatrix + s2)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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{
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vectype tmp = state1[j] + state2[j];
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state[j] ^= tmp;
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}
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round_lyra_v5(state);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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{
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state1[j] ^= state[j];
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(DMatrix + s3)[j] = state1[j];
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}
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#endif
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((uint2*)state2)[0] ^= ((uint2*)state)[11];
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#pragma unroll
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for (int j = 0; j < 11; j++)
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((uint2*)state2)[j+1] ^= ((uint2*)state)[j];
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state2[j];
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}
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}
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static __device__ __forceinline__
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void reduceDuplexRowSetupV3(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
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{
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vectype state2[3], state1[3];
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uint32_t ps1 = (memshift * rowIn + Nrow * Ncol * memshift * thread);
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uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
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uint32_t ps3 = (Nrow * memshift * (Ncol - 1) + memshift * rowOut + Nrow * Ncol * memshift * thread);
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for (int i = 0; i < Ncol; i++)
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{
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uint32_t s1 = ps1 + Nrow*i*memshift;
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uint32_t s2 = ps2 + Nrow*i*memshift;
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uint32_t s3 = ps3 - Nrow*i*memshift;
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix + s1 )[j]);
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for (int j = 0; j < 3; j++)
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state2[j] = __ldg4(&(DMatrix + s2 )[j]);
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for (int j = 0; j < 3; j++) {
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vectype tmp = state1[j] + state2[j];
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state[j] ^= tmp;
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}
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round_lyra_v35(state);
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for (int j = 0; j < 3; j++) {
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state1[j] ^= state[j];
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(DMatrix + s3)[j] = state1[j];
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}
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((uint2*)state2)[0] ^= ((uint2*)state)[11];
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for (int j = 0; j < 11; j++)
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((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
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#pragma unroll
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state2[j];
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}
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}
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static __device__ __forceinline__
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__device__ __forceinline__
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void reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
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{
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vectype state1[3],state2[3];
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uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread);
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uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread);
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uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread);
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uint32_t ps3 = (memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread);
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uint32_t ps3 = (memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread);
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//#pragma unroll 1
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for (int i = 0; i < Ncol; i++)
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{
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uint32_t s1 = ps1 + i*memshift;
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uint32_t s2 = ps2 + i*memshift;
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uint32_t s3 = ps3 + i*memshift;
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix + s1)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state2[j] = __ldg4(&(DMatrix + s2)[j]);
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state1[j] += state2[j];
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state[j] ^= state1[j];
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round_lyra_v35(state);
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((uint2*)state2)[0] ^= ((uint2*)state)[11];
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for (int j = 0; j < 11; j++)
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((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
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if (rowInOut != rowOut) {
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state2[j];
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for (int j = 0; j < 3; j++)
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(DMatrix + s3)[j] ^= state[j];
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} else {
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for (int j = 0; j < 3; j++)
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state2[j] ^= state[j];
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j]=state2[j];
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}
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}
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}
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static __device__ __forceinline__
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void reduceDuplexRowtV3(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
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{
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vectype state1[3], state2[3];
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uint32_t ps1 = (memshift * rowIn + Nrow * Ncol * memshift * thread);
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uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
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uint32_t ps3 = (memshift * rowOut + Nrow * Ncol * memshift * thread);
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#pragma nounroll
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for (int i = 0; i < Ncol; i++)
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{
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uint32_t s1 = ps1 + Nrow * i*memshift;
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uint32_t s2 = ps2 + Nrow * i*memshift;
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uint32_t s3 = ps3 + Nrow * i*memshift;
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for (int j = 0; j < 3; j++)
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state1[j] = __ldg4(&(DMatrix + s1)[j]);
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for (int j = 0; j < 3; j++)
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state2[j] = __ldg4(&(DMatrix + s2)[j]);
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for (int j = 0; j < 3; j++)
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state1[j] += state2[j];
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for (int j = 0; j < 3; j++)
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state[j] ^= state1[j];
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round_lyra_v35(state);
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round_lyra_v5(state);
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((uint2*)state2)[0] ^= ((uint2*)state)[11];
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#pragma unroll
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for (int j = 0; j < 11; j++)
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((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
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if (rowInOut != rowOut) {
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state2[j];
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#if __CUDA_ARCH__ == 500
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if (rowInOut != rowOut)
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{
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#pragma unroll
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for (int j = 0; j < 3; j++)
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(DMatrix + s3)[j] ^= state[j];
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} else {
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}
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if (rowInOut == rowOut)
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{
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#pragma unroll
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for (int j = 0; j < 3; j++)
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state2[j] ^= state[j];
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for (int j = 0; j < 3; j++)
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(DMatrix + s2)[j] = state2[j];
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}
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}
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}
|
||||
|
||||
|
||||
#if __CUDA_ARCH__ < 500
|
||||
__global__ __launch_bounds__(128, 1)
|
||||
#elif __CUDA_ARCH__ == 500
|
||||
__global__ __launch_bounds__(16, 1)
|
||||
#else
|
||||
__global__ __launch_bounds__(TPB, 1)
|
||||
#endif
|
||||
void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
|
||||
{
|
||||
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
|
||||
|
||||
vectype state[4];
|
||||
uint28 blake2b_IV[2];
|
||||
uint28 padding[2];
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
|
||||
((uint16*)blake2b_IV)[0] = make_uint16(
|
||||
0xf3bcc908, 0x6a09e667 , 0x84caa73b, 0xbb67ae85 ,
|
||||
0xfe94f82b, 0x3c6ef372 , 0x5f1d36f1, 0xa54ff53a ,
|
||||
0xade682d1, 0x510e527f , 0x2b3e6c1f, 0x9b05688c ,
|
||||
0xfb41bd6b, 0x1f83d9ab , 0x137e2179, 0x5be0cd19
|
||||
);
|
||||
((uint16*)padding)[0] = make_uint16(
|
||||
0x20, 0x0 , 0x20, 0x0 , 0x20, 0x0 , 0x01, 0x0 ,
|
||||
0x04, 0x0 , 0x04, 0x0 , 0x80, 0x0 , 0x0, 0x01000000
|
||||
);
|
||||
}
|
||||
|
||||
#if __CUDA_ARCH__ <= 350
|
||||
if (thread < threads)
|
||||
#endif
|
||||
{
|
||||
((uint2*)state)[0] = __ldg(&outputHash[thread]);
|
||||
((uint2*)state)[1] = __ldg(&outputHash[thread + threads]);
|
||||
((uint2*)state)[2] = __ldg(&outputHash[thread + 2 * threads]);
|
||||
((uint2*)state)[3] = __ldg(&outputHash[thread + 3 * threads]);
|
||||
state[1] = state[0];
|
||||
state[2] = shuffle4(((vectype*)blake2b_IV)[0], 0);
|
||||
state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0);
|
||||
|
||||
for (int i = 0; i<12; i++)
|
||||
round_lyra_v35(state);
|
||||
|
||||
state[0] ^= shuffle4(((vectype*)padding)[0], 0);
|
||||
state[1] ^= shuffle4(((vectype*)padding)[1], 0);
|
||||
|
||||
for (int i = 0; i<12; i++)
|
||||
round_lyra_v35(state);
|
||||
|
||||
uint32_t ps1 = (4 * memshift * 3 + 16 * memshift * thread);
|
||||
|
||||
//#pragma unroll 4
|
||||
for (int i = 0; i < 4; i++)
|
||||
if (rowInOut != rowOut)
|
||||
{
|
||||
uint32_t s1 = ps1 - 4 * memshift * i;
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s1)[j] = (state)[j];
|
||||
|
||||
round_lyra_v35(state);
|
||||
(DMatrix + s3)[j] ^= state[j];
|
||||
} else {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 3; j++)
|
||||
state2[j] ^= state[j];
|
||||
}
|
||||
|
||||
reduceDuplexV3(state, thread);
|
||||
reduceDuplexRowSetupV3(1, 0, 2, state, thread);
|
||||
reduceDuplexRowSetupV3(2, 1, 3, state, thread);
|
||||
|
||||
uint32_t rowa;
|
||||
int prev = 3;
|
||||
for (int i = 0; i < 4; i++)
|
||||
{
|
||||
rowa = ((uint2*)state)[0].x & 3; reduceDuplexRowtV3(prev, rowa, i, state, thread);
|
||||
prev = i;
|
||||
}
|
||||
|
||||
uint32_t shift = (memshift * rowa + 16 * memshift * thread);
|
||||
|
||||
#endif
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 3; j++)
|
||||
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
|
||||
|
||||
for (int i = 0; i < 12; i++)
|
||||
round_lyra_v35(state);
|
||||
|
||||
outputHash[thread] = ((uint2*)state)[0];
|
||||
outputHash[thread + threads] = ((uint2*)state)[1];
|
||||
outputHash[thread + 2 * threads] = ((uint2*)state)[2];
|
||||
outputHash[thread + 3 * threads] = ((uint2*)state)[3];
|
||||
//((vectype*)outputHash)[thread] = state[0];
|
||||
|
||||
} //thread
|
||||
(DMatrix + s2)[j] = state2[j];
|
||||
}
|
||||
}
|
||||
|
||||
#if __CUDA_ARCH__ < 500
|
||||
__global__ __launch_bounds__(64, 1)
|
||||
#elif __CUDA_ARCH__ == 500
|
||||
__global__ __launch_bounds__(32, 1)
|
||||
|
||||
#if __CUDA_ARCH__ == 500
|
||||
__global__ __launch_bounds__(TPB50, 1)
|
||||
#else
|
||||
__global__ __launch_bounds__(TPB, 1)
|
||||
__global__ __launch_bounds__(TPB52, 1)
|
||||
#endif
|
||||
void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
|
||||
{
|
||||
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
|
||||
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
|
||||
|
||||
vectype state[4];
|
||||
|
||||
uint28 blake2b_IV[2];
|
||||
uint28 padding[2];
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
|
||||
((uint16*)blake2b_IV)[0] = make_uint16(
|
||||
0xf3bcc908, 0x6a09e667, 0x84caa73b, 0xbb67ae85,
|
||||
0xfe94f82b, 0x3c6ef372, 0x5f1d36f1, 0xa54ff53a,
|
||||
0xade682d1, 0x510e527f, 0x2b3e6c1f, 0x9b05688c,
|
||||
0xfb41bd6b, 0x1f83d9ab, 0x137e2179, 0x5be0cd19
|
||||
);
|
||||
((uint16*)padding)[0] = make_uint16(
|
||||
0x20, 0x0, 0x20, 0x0, 0x20, 0x0, 0x01, 0x0,
|
||||
0x04, 0x0, 0x04, 0x0, 0x80, 0x0, 0x0, 0x01000000
|
||||
0xf3bcc908, 0x6a09e667, 0x84caa73b, 0xbb67ae85,
|
||||
0xfe94f82b, 0x3c6ef372, 0x5f1d36f1, 0xa54ff53a,
|
||||
0xade682d1, 0x510e527f, 0x2b3e6c1f, 0x9b05688c,
|
||||
0xfb41bd6b, 0x1f83d9ab, 0x137e2179, 0x5be0cd19
|
||||
);
|
||||
}
|
||||
|
||||
#if __CUDA_ARCH__ <= 350
|
||||
if (thread < threads)
|
||||
#endif
|
||||
{
|
||||
((uint2*)state)[0] = __ldg(&outputHash[thread]);
|
||||
((uint2*)state)[1] = __ldg(&outputHash[thread + threads]);
|
||||
@ -444,61 +286,67 @@ void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHa
|
||||
|
||||
state[1] = state[0];
|
||||
|
||||
state[2] = shuffle4(((vectype*)blake2b_IV)[0], 0);
|
||||
state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0);
|
||||
state[2] = ((blake2b_IV)[0]);
|
||||
state[3] = ((blake2b_IV)[1]);
|
||||
|
||||
for (int i = 0; i<12; i++)
|
||||
round_lyra_v35(state);
|
||||
round_lyra_v5(state);
|
||||
|
||||
state[0] ^= shuffle4(((vectype*)padding)[0], 0);
|
||||
state[1] ^= shuffle4(((vectype*)padding)[1], 0);
|
||||
((uint2*)state)[0].x ^= 0x20;
|
||||
((uint2*)state)[1].x ^= 0x20;
|
||||
((uint2*)state)[2].x ^= 0x20;
|
||||
((uint2*)state)[3].x ^= 0x01;
|
||||
((uint2*)state)[4].x ^= 0x04;
|
||||
((uint2*)state)[5].x ^= 0x04;
|
||||
((uint2*)state)[6].x ^= 0x80;
|
||||
((uint2*)state)[7].y ^= 0x01000000;
|
||||
|
||||
for (int i = 0; i<12; i++)
|
||||
round_lyra_v35(state);
|
||||
round_lyra_v5(state);
|
||||
|
||||
uint32_t ps1 = (memshift * (Ncol - 1) + Nrow * Ncol * memshift * thread);
|
||||
|
||||
for (int i = 0; i < Ncol; i++)
|
||||
{
|
||||
uint32_t s1 = ps1 - memshift * i;
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s1)[j] = (state)[j];
|
||||
|
||||
round_lyra_v35(state);
|
||||
const uint32_t s1 = ps1 - memshift * i;
|
||||
DMatrix[s1] = state[0];
|
||||
DMatrix[s1+1] = state[1];
|
||||
DMatrix[s1+2] = state[2];
|
||||
round_lyra_v5(state);
|
||||
}
|
||||
|
||||
reduceDuplex(state, thread);
|
||||
reduceDuplex50(state, thread);
|
||||
|
||||
reduceDuplexRowSetupV2(1, 0, 2, state, thread);
|
||||
reduceDuplexRowSetupV2(2, 1, 3, state, thread);
|
||||
reduceDuplexRowSetupV2(1, 0, 2, state, thread);
|
||||
reduceDuplexRowSetupV2(2, 1, 3, state, thread);
|
||||
|
||||
uint32_t rowa;
|
||||
int prev=3;
|
||||
|
||||
for (int i = 0; i < 4; i++) {
|
||||
for (int i = 0; i < 4; i++)
|
||||
{
|
||||
rowa = ((uint2*)state)[0].x & 3;
|
||||
reduceDuplexRowtV2(prev, rowa, i, state, thread);
|
||||
prev=i;
|
||||
}
|
||||
|
||||
uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread);
|
||||
const uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread);
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < 3; j++)
|
||||
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
|
||||
|
||||
for (int i = 0; i < 12; i++)
|
||||
round_lyra_v35(state);
|
||||
round_lyra_v5(state);
|
||||
|
||||
outputHash[thread] = ((uint2*)state)[0];
|
||||
outputHash[thread] = ((uint2*)state)[0];
|
||||
outputHash[thread + threads] = ((uint2*)state)[1];
|
||||
outputHash[thread + 2 * threads] = ((uint2*)state)[2];
|
||||
outputHash[thread + 3 * threads] = ((uint2*)state)[3];
|
||||
}
|
||||
}
|
||||
#else
|
||||
/* if __CUDA_ARCH__ < 300 .. */
|
||||
__global__ void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {}
|
||||
__global__ void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {}
|
||||
#endif
|
||||
|
||||
__host__
|
||||
@ -512,12 +360,14 @@ __host__
|
||||
void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order)
|
||||
{
|
||||
uint32_t tpb;
|
||||
if (device_sm[device_map[thr_id]] == 350)
|
||||
tpb = 64;
|
||||
if (device_sm[device_map[thr_id]] < 350)
|
||||
tpb = TPB30;
|
||||
else if (device_sm[device_map[thr_id]] == 350)
|
||||
tpb = TPB35;
|
||||
else if (device_sm[device_map[thr_id]] == 500)
|
||||
tpb = 32;
|
||||
tpb = TPB50;
|
||||
else
|
||||
tpb = TPB;
|
||||
tpb = TPB52;
|
||||
|
||||
dim3 grid((threads + tpb - 1) / tpb);
|
||||
dim3 block(tpb);
|
||||
@ -527,6 +377,5 @@ void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uin
|
||||
else
|
||||
lyra2v2_gpu_hash_32_v3 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash);
|
||||
|
||||
MyStreamSynchronize(NULL, order, thr_id);
|
||||
//MyStreamSynchronize(NULL, order, thr_id);
|
||||
}
|
||||
|
||||
|
||||
253
lyra2/cuda_lyra2v2_sm3.cuh
Normal file
253
lyra2/cuda_lyra2v2_sm3.cuh
Normal file
@ -0,0 +1,253 @@
|
||||
/* SM 3/3.5 Variant for lyra2REv2 */
|
||||
|
||||
#ifdef __INTELLISENSE__
|
||||
/* just for vstudio code colors */
|
||||
#undef __CUDA_ARCH__
|
||||
#define __CUDA_ARCH__ 350
|
||||
#endif
|
||||
|
||||
#define TPB30 16
|
||||
#define TPB35 64
|
||||
|
||||
#if __CUDA_ARCH__ >= 300 && __CUDA_ARCH__ < 500
|
||||
|
||||
#define Nrow 4
|
||||
#define Ncol 4
|
||||
|
||||
#define vectype ulonglong4
|
||||
#define u64type uint64_t
|
||||
#define memshift 4
|
||||
|
||||
__device__ vectype *DMatrix;
|
||||
|
||||
static __device__ __forceinline__
|
||||
void Gfunc_v35(unsigned long long &a, unsigned long long &b, unsigned long long &c, unsigned long long &d)
|
||||
{
|
||||
a += b; d ^= a; d = ROTR64(d, 32);
|
||||
c += d; b ^= c; b = ROTR64(b, 24);
|
||||
a += b; d ^= a; d = ROTR64(d, 16);
|
||||
c += d; b ^= c; b = ROTR64(b, 63);
|
||||
}
|
||||
|
||||
static __device__ __forceinline__
|
||||
void round_lyra_v35(vectype* s)
|
||||
{
|
||||
Gfunc_v35(s[0].x, s[1].x, s[2].x, s[3].x);
|
||||
Gfunc_v35(s[0].y, s[1].y, s[2].y, s[3].y);
|
||||
Gfunc_v35(s[0].z, s[1].z, s[2].z, s[3].z);
|
||||
Gfunc_v35(s[0].w, s[1].w, s[2].w, s[3].w);
|
||||
|
||||
Gfunc_v35(s[0].x, s[1].y, s[2].z, s[3].w);
|
||||
Gfunc_v35(s[0].y, s[1].z, s[2].w, s[3].x);
|
||||
Gfunc_v35(s[0].z, s[1].w, s[2].x, s[3].y);
|
||||
Gfunc_v35(s[0].w, s[1].x, s[2].y, s[3].z);
|
||||
}
|
||||
|
||||
static __device__ __forceinline__
|
||||
void reduceDuplexV3(vectype state[4], uint32_t thread)
|
||||
{
|
||||
vectype state1[3];
|
||||
uint32_t ps1 = (Nrow * Ncol * memshift * thread);
|
||||
uint32_t ps2 = (memshift * (Ncol - 1) * Nrow + memshift * 1 + Nrow * Ncol * memshift * thread);
|
||||
|
||||
#pragma unroll 4
|
||||
for (int i = 0; i < Ncol; i++)
|
||||
{
|
||||
uint32_t s1 = ps1 + Nrow * i *memshift;
|
||||
uint32_t s2 = ps2 - Nrow * i *memshift;
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state1[j] = __ldg4(&(DMatrix + s1)[j]);
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state[j] ^= state1[j];
|
||||
round_lyra_v35(state);
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state1[j] ^= state[j];
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s2)[j] = state1[j];
|
||||
}
|
||||
}
|
||||
|
||||
static __device__ __forceinline__
|
||||
void reduceDuplexRowSetupV3(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
|
||||
{
|
||||
vectype state2[3], state1[3];
|
||||
|
||||
uint32_t ps1 = (memshift * rowIn + Nrow * Ncol * memshift * thread);
|
||||
uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
|
||||
uint32_t ps3 = (Nrow * memshift * (Ncol - 1) + memshift * rowOut + Nrow * Ncol * memshift * thread);
|
||||
|
||||
for (int i = 0; i < Ncol; i++)
|
||||
{
|
||||
uint32_t s1 = ps1 + Nrow*i*memshift;
|
||||
uint32_t s2 = ps2 + Nrow*i*memshift;
|
||||
uint32_t s3 = ps3 - Nrow*i*memshift;
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state1[j] = __ldg4(&(DMatrix + s1 )[j]);
|
||||
for (int j = 0; j < 3; j++)
|
||||
state2[j] = __ldg4(&(DMatrix + s2 )[j]);
|
||||
for (int j = 0; j < 3; j++) {
|
||||
vectype tmp = state1[j] + state2[j];
|
||||
state[j] ^= tmp;
|
||||
}
|
||||
|
||||
round_lyra_v35(state);
|
||||
|
||||
for (int j = 0; j < 3; j++) {
|
||||
state1[j] ^= state[j];
|
||||
(DMatrix + s3)[j] = state1[j];
|
||||
}
|
||||
|
||||
((uint2*)state2)[0] ^= ((uint2*)state)[11];
|
||||
for (int j = 0; j < 11; j++)
|
||||
((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s2)[j] = state2[j];
|
||||
}
|
||||
}
|
||||
|
||||
static __device__ __forceinline__
|
||||
void reduceDuplexRowtV3(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
|
||||
{
|
||||
vectype state1[3], state2[3];
|
||||
uint32_t ps1 = (memshift * rowIn + Nrow * Ncol * memshift * thread);
|
||||
uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
|
||||
uint32_t ps3 = (memshift * rowOut + Nrow * Ncol * memshift * thread);
|
||||
|
||||
#pragma nounroll
|
||||
for (int i = 0; i < Ncol; i++)
|
||||
{
|
||||
uint32_t s1 = ps1 + Nrow * i*memshift;
|
||||
uint32_t s2 = ps2 + Nrow * i*memshift;
|
||||
uint32_t s3 = ps3 + Nrow * i*memshift;
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state1[j] = __ldg4(&(DMatrix + s1)[j]);
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state2[j] = __ldg4(&(DMatrix + s2)[j]);
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state1[j] += state2[j];
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state[j] ^= state1[j];
|
||||
|
||||
round_lyra_v35(state);
|
||||
|
||||
((uint2*)state2)[0] ^= ((uint2*)state)[11];
|
||||
|
||||
for (int j = 0; j < 11; j++)
|
||||
((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
|
||||
|
||||
if (rowInOut != rowOut) {
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s2)[j] = state2[j];
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s3)[j] ^= state[j];
|
||||
|
||||
} else {
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state2[j] ^= state[j];
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s2)[j] = state2[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__global__ __launch_bounds__(TPB35, 1)
|
||||
void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
|
||||
{
|
||||
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
|
||||
|
||||
vectype state[4];
|
||||
uint28 blake2b_IV[2];
|
||||
uint28 padding[2];
|
||||
|
||||
if (threadIdx.x == 0) {
|
||||
|
||||
((uint16*)blake2b_IV)[0] = make_uint16(
|
||||
0xf3bcc908, 0x6a09e667 , 0x84caa73b, 0xbb67ae85 ,
|
||||
0xfe94f82b, 0x3c6ef372 , 0x5f1d36f1, 0xa54ff53a ,
|
||||
0xade682d1, 0x510e527f , 0x2b3e6c1f, 0x9b05688c ,
|
||||
0xfb41bd6b, 0x1f83d9ab , 0x137e2179, 0x5be0cd19
|
||||
);
|
||||
((uint16*)padding)[0] = make_uint16(
|
||||
0x20, 0x0 , 0x20, 0x0 , 0x20, 0x0 , 0x01, 0x0 ,
|
||||
0x04, 0x0 , 0x04, 0x0 , 0x80, 0x0 , 0x0, 0x01000000
|
||||
);
|
||||
}
|
||||
|
||||
if (thread < threads)
|
||||
{
|
||||
((uint2*)state)[0] = __ldg(&outputHash[thread]);
|
||||
((uint2*)state)[1] = __ldg(&outputHash[thread + threads]);
|
||||
((uint2*)state)[2] = __ldg(&outputHash[thread + 2 * threads]);
|
||||
((uint2*)state)[3] = __ldg(&outputHash[thread + 3 * threads]);
|
||||
state[1] = state[0];
|
||||
state[2] = shuffle4(((vectype*)blake2b_IV)[0], 0);
|
||||
state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0);
|
||||
|
||||
for (int i = 0; i<12; i++)
|
||||
round_lyra_v35(state);
|
||||
|
||||
state[0] ^= shuffle4(((vectype*)padding)[0], 0);
|
||||
state[1] ^= shuffle4(((vectype*)padding)[1], 0);
|
||||
|
||||
for (int i = 0; i<12; i++)
|
||||
round_lyra_v35(state);
|
||||
|
||||
uint32_t ps1 = (4 * memshift * 3 + 16 * memshift * thread);
|
||||
|
||||
//#pragma unroll 4
|
||||
for (int i = 0; i < 4; i++)
|
||||
{
|
||||
uint32_t s1 = ps1 - 4 * memshift * i;
|
||||
for (int j = 0; j < 3; j++)
|
||||
(DMatrix + s1)[j] = (state)[j];
|
||||
|
||||
round_lyra_v35(state);
|
||||
}
|
||||
|
||||
reduceDuplexV3(state, thread);
|
||||
reduceDuplexRowSetupV3(1, 0, 2, state, thread);
|
||||
reduceDuplexRowSetupV3(2, 1, 3, state, thread);
|
||||
|
||||
uint32_t rowa;
|
||||
int prev = 3;
|
||||
for (int i = 0; i < 4; i++)
|
||||
{
|
||||
rowa = ((uint2*)state)[0].x & 3; reduceDuplexRowtV3(prev, rowa, i, state, thread);
|
||||
prev = i;
|
||||
}
|
||||
|
||||
uint32_t shift = (memshift * rowa + 16 * memshift * thread);
|
||||
|
||||
for (int j = 0; j < 3; j++)
|
||||
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
|
||||
|
||||
for (int i = 0; i < 12; i++)
|
||||
round_lyra_v35(state);
|
||||
|
||||
outputHash[thread] = ((uint2*)state)[0];
|
||||
outputHash[thread + threads] = ((uint2*)state)[1];
|
||||
outputHash[thread + 2 * threads] = ((uint2*)state)[2];
|
||||
outputHash[thread + 3 * threads] = ((uint2*)state)[3];
|
||||
|
||||
} //thread
|
||||
}
|
||||
|
||||
#else
|
||||
/* if __CUDA_ARCH__ < 300 .. */
|
||||
__global__ void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {}
|
||||
#endif
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user