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526 lines
13 KiB
526 lines
13 KiB
#include <stdio.h> |
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#include <memory.h> |
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#ifdef __INTELLISENSE__ |
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/* just for vstudio code colors */ |
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#define __CUDA_ARCH__ 500 |
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#endif |
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#include "cuda_lyra2_vectors.h" |
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#define TPB 16 |
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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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{ |
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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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{ |
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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_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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} |
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static __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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uint32_t ps1 = (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 + i*memshift; |
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uint32_t s2 = ps2 - 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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state[j] ^= state1[j]; |
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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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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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{ |
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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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#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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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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state[j] ^= state1[j]; |
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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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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 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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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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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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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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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 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 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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//#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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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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((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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((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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#if __CUDA_ARCH__ < 500 |
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__global__ __launch_bounds__(128, 1) |
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#elif __CUDA_ARCH__ == 500 |
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__global__ __launch_bounds__(16, 1) |
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#else |
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__global__ __launch_bounds__(TPB, 1) |
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#endif |
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void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) |
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{ |
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uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); |
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vectype state[4]; |
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uint28 blake2b_IV[2]; |
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uint28 padding[2]; |
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if (threadIdx.x == 0) { |
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((uint16*)blake2b_IV)[0] = make_uint16( |
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0xf3bcc908, 0x6a09e667 , 0x84caa73b, 0xbb67ae85 , |
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0xfe94f82b, 0x3c6ef372 , 0x5f1d36f1, 0xa54ff53a , |
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0xade682d1, 0x510e527f , 0x2b3e6c1f, 0x9b05688c , |
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0xfb41bd6b, 0x1f83d9ab , 0x137e2179, 0x5be0cd19 |
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); |
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((uint16*)padding)[0] = make_uint16( |
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0x20, 0x0 , 0x20, 0x0 , 0x20, 0x0 , 0x01, 0x0 , |
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0x04, 0x0 , 0x04, 0x0 , 0x80, 0x0 , 0x0, 0x01000000 |
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); |
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} |
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#if __CUDA_ARCH__ <= 350 |
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if (thread < threads) |
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#endif |
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{ |
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((uint2*)state)[0] = __ldg(&outputHash[thread]); |
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((uint2*)state)[1] = __ldg(&outputHash[thread + threads]); |
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((uint2*)state)[2] = __ldg(&outputHash[thread + 2 * threads]); |
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((uint2*)state)[3] = __ldg(&outputHash[thread + 3 * threads]); |
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state[1] = state[0]; |
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state[2] = shuffle4(((vectype*)blake2b_IV)[0], 0); |
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state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0); |
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for (int i = 0; i<12; i++) |
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round_lyra_v35(state); |
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state[0] ^= shuffle4(((vectype*)padding)[0], 0); |
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state[1] ^= shuffle4(((vectype*)padding)[1], 0); |
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for (int i = 0; i<12; i++) |
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round_lyra_v35(state); |
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uint32_t ps1 = (4 * memshift * 3 + 16 * memshift * thread); |
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//#pragma unroll 4 |
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for (int i = 0; i < 4; i++) |
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{ |
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uint32_t s1 = ps1 - 4 * memshift * i; |
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for (int j = 0; j < 3; j++) |
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(DMatrix + s1)[j] = (state)[j]; |
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round_lyra_v35(state); |
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} |
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reduceDuplexV3(state, thread); |
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reduceDuplexRowSetupV3(1, 0, 2, state, thread); |
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reduceDuplexRowSetupV3(2, 1, 3, state, thread); |
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uint32_t rowa; |
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int prev = 3; |
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for (int i = 0; i < 4; i++) |
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{ |
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rowa = ((uint2*)state)[0].x & 3; reduceDuplexRowtV3(prev, rowa, i, state, thread); |
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prev = i; |
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} |
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uint32_t shift = (memshift * rowa + 16 * memshift * thread); |
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for (int j = 0; j < 3; j++) |
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state[j] ^= __ldg4(&(DMatrix + shift)[j]); |
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for (int i = 0; i < 12; i++) |
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round_lyra_v35(state); |
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outputHash[thread] = ((uint2*)state)[0]; |
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outputHash[thread + threads] = ((uint2*)state)[1]; |
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outputHash[thread + 2 * threads] = ((uint2*)state)[2]; |
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outputHash[thread + 3 * threads] = ((uint2*)state)[3]; |
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//((vectype*)outputHash)[thread] = state[0]; |
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} //thread |
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} |
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#if __CUDA_ARCH__ < 500 |
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__global__ __launch_bounds__(64, 1) |
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#elif __CUDA_ARCH__ == 500 |
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__global__ __launch_bounds__(32, 1) |
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#else |
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__global__ __launch_bounds__(TPB, 1) |
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#endif |
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void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash) |
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{ |
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uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); |
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vectype state[4]; |
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uint28 blake2b_IV[2]; |
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uint28 padding[2]; |
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if (threadIdx.x == 0) { |
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((uint16*)blake2b_IV)[0] = make_uint16( |
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0xf3bcc908, 0x6a09e667 , 0x84caa73b, 0xbb67ae85 , |
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0xfe94f82b, 0x3c6ef372 , 0x5f1d36f1, 0xa54ff53a , |
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0xade682d1, 0x510e527f , 0x2b3e6c1f, 0x9b05688c , |
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0xfb41bd6b, 0x1f83d9ab , 0x137e2179, 0x5be0cd19 |
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); |
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((uint16*)padding)[0] = make_uint16( |
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0x20, 0x0 , 0x20, 0x0 , 0x20, 0x0 , 0x01, 0x0 , |
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0x04, 0x0 , 0x04, 0x0 , 0x80, 0x0 , 0x0, 0x01000000 |
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); |
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} |
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#if __CUDA_ARCH__ <= 350 |
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if (thread < threads) |
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#endif |
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{ |
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((uint2*)state)[0] = __ldg(&outputHash[thread]); |
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((uint2*)state)[1] = __ldg(&outputHash[thread + threads]); |
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((uint2*)state)[2] = __ldg(&outputHash[thread + 2 * threads]); |
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((uint2*)state)[3] = __ldg(&outputHash[thread + 3 * threads]); |
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state[1] = state[0]; |
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state[2] = shuffle4(((vectype*)blake2b_IV)[0], 0); |
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state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0); |
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for (int i = 0; i<12; i++) |
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round_lyra_v35(state); |
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state[0] ^= shuffle4(((vectype*)padding)[0], 0); |
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state[1] ^= shuffle4(((vectype*)padding)[1], 0); |
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for (int i = 0; i<12; i++) |
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round_lyra_v35(state); |
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uint32_t ps1 = (memshift * (Ncol - 1) + 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 - memshift * i; |
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for (int j = 0; j < 3; j++) |
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(DMatrix + s1)[j] = (state)[j]; |
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round_lyra_v35(state); |
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} |
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reduceDuplex(state, thread); |
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reduceDuplexRowSetupV2(1, 0, 2, state, thread); |
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reduceDuplexRowSetupV2(2, 1, 3, state, thread); |
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uint32_t rowa; |
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int prev=3; |
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for (int i = 0; i < 4; i++) { |
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rowa = ((uint2*)state)[0].x & 3; |
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reduceDuplexRowtV2(prev, rowa, i, state, thread); |
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prev=i; |
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} |
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uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread); |
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for (int j = 0; j < 3; j++) |
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state[j] ^= __ldg4(&(DMatrix + shift)[j]); |
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for (int i = 0; i < 12; i++) |
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round_lyra_v35(state); |
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outputHash[thread] = ((uint2*)state)[0]; |
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outputHash[thread + threads] = ((uint2*)state)[1]; |
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outputHash[thread + 2 * threads] = ((uint2*)state)[2]; |
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outputHash[thread + 3 * threads] = ((uint2*)state)[3]; |
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} |
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} |
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#else |
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/* if __CUDA_ARCH__ < 300 .. */ |
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__global__ void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {} |
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__global__ void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {} |
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#endif |
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__host__ |
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void lyra2v2_cpu_init(int thr_id, uint32_t threads,uint64_t *hash) |
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{ |
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cudaMemcpyToSymbol(DMatrix, &hash, sizeof(hash), 0, cudaMemcpyHostToDevice); |
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} |
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__host__ |
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void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order) |
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{ |
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uint32_t tpb; |
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if (device_sm[device_map[thr_id]] == 350) |
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tpb = 64; |
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else if (device_sm[device_map[thr_id]] == 500) |
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tpb = 32; |
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else |
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tpb = TPB; |
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dim3 grid((threads + tpb - 1) / tpb); |
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dim3 block(tpb); |
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if (device_sm[device_map[thr_id]] >= 500) |
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lyra2v2_gpu_hash_32 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash); |
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else |
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lyra2v2_gpu_hash_32_v3 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash); |
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MyStreamSynchronize(NULL, order, thr_id); |
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} |
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