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381 lines
8.9 KiB
381 lines
8.9 KiB
#include <stdio.h> |
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#include <stdint.h> |
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#include <memory.h> |
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#define TPB52 10 |
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#define TPB50 16 |
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#include "cuda_lyra2v2_sm3.cuh" |
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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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#if __CUDA_ARCH__ >= 500 |
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#include "cuda_lyra2_vectors.h" |
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#define Nrow 4 |
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#define Ncol 4 |
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#define uint4x2 uint28 |
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#define memshift 3 |
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__device__ uint4x2 *DMatrix; |
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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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__device__ __forceinline__ |
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void round_lyra_v5(uint4x2* s) |
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{ |
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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_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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__device__ __forceinline__ |
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void reduceDuplex(uint4x2 state[4], uint32_t thread) |
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{ |
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uint4x2 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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#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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state[j] ^= state1[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] ^= 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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__device__ __forceinline__ |
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void reduceDuplex50(uint4x2 state[4], uint32_t thread) |
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{ |
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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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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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state[j] ^= __ldg4(&(DMatrix + s1)[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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(DMatrix + s2)[j] = __ldg4(&(DMatrix + s1)[j]) ^ state[j]; |
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} |
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} |
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__device__ __forceinline__ |
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void reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, uint4x2 state[4], uint32_t thread) |
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{ |
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uint4x2 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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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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#if __CUDA_ARCH__ == 500 |
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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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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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uint4x2 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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#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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__device__ __forceinline__ |
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void reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, uint4x2* state, uint32_t thread) |
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{ |
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uint4x2 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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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_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 __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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} |
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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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} |
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#else |
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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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#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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} |
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#endif |
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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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#if __CUDA_ARCH__ == 500 |
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__global__ __launch_bounds__(TPB50, 1) |
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#else |
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__global__ __launch_bounds__(TPB52, 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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const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); |
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uint4x2 state[4]; |
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uint4x2 blake2b_IV[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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} |
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if (thread < threads) |
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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] = ((blake2b_IV)[0]); |
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state[3] = ((blake2b_IV)[1]); |
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for (int i = 0; i<12; i++) |
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round_lyra_v5(state); |
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((uint2*)state)[0].x ^= 0x20; |
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((uint2*)state)[1].x ^= 0x20; |
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((uint2*)state)[2].x ^= 0x20; |
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((uint2*)state)[3].x ^= 0x01; |
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((uint2*)state)[4].x ^= 0x04; |
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((uint2*)state)[5].x ^= 0x04; |
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((uint2*)state)[6].x ^= 0x80; |
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((uint2*)state)[7].y ^= 0x01000000; |
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for (int i = 0; i<12; i++) |
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round_lyra_v5(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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const uint32_t s1 = ps1 - memshift * i; |
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DMatrix[s1] = state[0]; |
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DMatrix[s1+1] = state[1]; |
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DMatrix[s1+2] = state[2]; |
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round_lyra_v5(state); |
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} |
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reduceDuplex50(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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{ |
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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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const uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread); |
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#pragma unroll |
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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_v5(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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#include "cuda_helper.h" |
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#if __CUDA_ARCH__ < 300 |
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__device__ void* DMatrix; |
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#endif |
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__global__ void lyra2v2_gpu_hash_32(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 *d_matrix) |
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{ |
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// just assign the device pointer allocated in main loop |
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cudaMemcpyToSymbol(DMatrix, &d_matrix, sizeof(uint64_t*), 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 = TPB30; |
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else if (device_sm[device_map[thr_id]] == 350) |
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tpb = TPB35; |
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else if (device_sm[device_map[thr_id]] == 500) |
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tpb = TPB50; |
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else |
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tpb = TPB52; |
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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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