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973 lines
26 KiB
973 lines
26 KiB
/** |
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* Lyra2 (v1) cuda implementation based on djm34 work |
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* tpruvot@github 2015, Nanashi 08/2016 (from 1.8-r2) |
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* Lyra2Z implentation for Zcoin based on all the previous |
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* djm34 2017 |
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**/ |
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#include <stdio.h> |
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#include <memory.h> |
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#define TPB52 32 |
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#define TPB30 160 |
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#define TPB20 160 |
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#include "cuda_lyra2Z_sm5.cuh" |
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#ifdef __INTELLISENSE__ |
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/* just for vstudio code colors */ |
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__device__ uint32_t __shfl(uint32_t a, uint32_t b, uint32_t c); |
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#define atomicMin() |
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#define __CUDA_ARCH__ 520 |
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#endif |
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static uint32_t *h_GNonces[16]; // this need to get fixed as the rest of that routine |
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static uint32_t *d_GNonces[16]; |
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#define reduceDuplexRow(rowIn, rowInOut, rowOut) { \ |
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for (int i = 0; i < 8; i++) { \ |
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for (int j = 0; j < 12; j++) \ |
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state[j] ^= Matrix[12 * i + j][rowIn] + Matrix[12 * i + j][rowInOut]; \ |
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round_lyra_sm2(state); \ |
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for (int j = 0; j < 12; j++) \ |
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Matrix[j + 12 * i][rowOut] ^= state[j]; \ |
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Matrix[0 + 12 * i][rowInOut] ^= state[11]; \ |
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Matrix[1 + 12 * i][rowInOut] ^= state[0]; \ |
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Matrix[2 + 12 * i][rowInOut] ^= state[1]; \ |
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Matrix[3 + 12 * i][rowInOut] ^= state[2]; \ |
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Matrix[4 + 12 * i][rowInOut] ^= state[3]; \ |
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Matrix[5 + 12 * i][rowInOut] ^= state[4]; \ |
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Matrix[6 + 12 * i][rowInOut] ^= state[5]; \ |
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Matrix[7 + 12 * i][rowInOut] ^= state[6]; \ |
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Matrix[8 + 12 * i][rowInOut] ^= state[7]; \ |
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Matrix[9 + 12 * i][rowInOut] ^= state[8]; \ |
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Matrix[10+ 12 * i][rowInOut] ^= state[9]; \ |
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Matrix[11+ 12 * i][rowInOut] ^= state[10]; \ |
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} \ |
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} |
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#define absorbblock(in) { \ |
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state[0] ^= Matrix[0][in]; \ |
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state[1] ^= Matrix[1][in]; \ |
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state[2] ^= Matrix[2][in]; \ |
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state[3] ^= Matrix[3][in]; \ |
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state[4] ^= Matrix[4][in]; \ |
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state[5] ^= Matrix[5][in]; \ |
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state[6] ^= Matrix[6][in]; \ |
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state[7] ^= Matrix[7][in]; \ |
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state[8] ^= Matrix[8][in]; \ |
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state[9] ^= Matrix[9][in]; \ |
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state[10] ^= Matrix[10][in]; \ |
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state[11] ^= Matrix[11][in]; \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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round_lyra_sm2(state); \ |
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} |
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__device__ __forceinline__ |
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static void round_lyra_sm2(uint2 *s) |
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{ |
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Gfunc(s[0], s[4], s[8], s[12]); |
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Gfunc(s[1], s[5], s[9], s[13]); |
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Gfunc(s[2], s[6], s[10], s[14]); |
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Gfunc(s[3], s[7], s[11], s[15]); |
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Gfunc(s[0], s[5], s[10], s[15]); |
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Gfunc(s[1], s[6], s[11], s[12]); |
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Gfunc(s[2], s[7], s[8], s[13]); |
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Gfunc(s[3], s[4], s[9], s[14]); |
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} |
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__device__ __forceinline__ |
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void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2 state[16], uint2 Matrix[96][8]) |
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{ |
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#if __CUDA_ARCH__ > 500 |
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#pragma unroll |
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#endif |
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for (int i = 0; i < 8; i++) |
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{ |
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#pragma unroll |
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for (int j = 0; j < 12; j++) |
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state[j] ^= Matrix[12 * i + j][rowIn] + Matrix[12 * i + j][rowInOut]; |
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round_lyra_sm2(state); |
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#pragma unroll |
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for (int j = 0; j < 12; j++) |
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Matrix[j + 84 - 12 * i][rowOut] = Matrix[12 * i + j][rowIn] ^ state[j]; |
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Matrix[0 + 12 * i][rowInOut] ^= state[11]; |
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Matrix[1 + 12 * i][rowInOut] ^= state[0]; |
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Matrix[2 + 12 * i][rowInOut] ^= state[1]; |
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Matrix[3 + 12 * i][rowInOut] ^= state[2]; |
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Matrix[4 + 12 * i][rowInOut] ^= state[3]; |
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Matrix[5 + 12 * i][rowInOut] ^= state[4]; |
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Matrix[6 + 12 * i][rowInOut] ^= state[5]; |
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Matrix[7 + 12 * i][rowInOut] ^= state[6]; |
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Matrix[8 + 12 * i][rowInOut] ^= state[7]; |
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Matrix[9 + 12 * i][rowInOut] ^= state[8]; |
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Matrix[10 + 12 * i][rowInOut] ^= state[9]; |
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Matrix[11 + 12 * i][rowInOut] ^= state[10]; |
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} |
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} |
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#if __CUDA_ARCH__ < 350 |
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__constant__ static uint2 blake2b_IV_sm2[8] = { |
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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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__global__ __launch_bounds__(TPB30, 1) |
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void lyra2Z_gpu_hash_32_sm2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash, uint32_t *resNonces) |
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{ |
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uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); |
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const uint2 Mask[8] = { |
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{ 0x00000020, 0x00000000 },{ 0x00000020, 0x00000000 }, |
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{ 0x00000020, 0x00000000 },{ 0x00000008, 0x00000000 }, |
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{ 0x00000008, 0x00000000 },{ 0x00000008, 0x00000000 }, |
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{ 0x00000080, 0x00000000 },{ 0x00000000, 0x01000000 } |
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}; |
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if (thread < threads) |
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{ |
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uint2 state[16]; |
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#pragma unroll |
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for (int i = 0; i<4; i++) { |
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LOHI(state[i].x, state[i].y, g_hash[threads*i + thread]); |
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} //password |
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#pragma unroll |
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for (int i = 0; i<4; i++) { |
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state[i + 4] = state[i]; |
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} //salt |
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#pragma unroll |
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for (int i = 0; i<8; i++) { |
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state[i + 8] = blake2b_IV_sm2[i]; |
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} |
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// blake2blyra x2 |
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//#pragma unroll 24 |
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for (int i = 0; i<12; i++) { |
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round_lyra_sm2(state); |
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} |
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for (int i = 0; i<8; i++) |
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state[i] ^= Mask[i]; |
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for (int i = 0; i<12; i++) { |
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round_lyra_sm2(state); |
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} |
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uint2 Matrix[96][8]; // not cool |
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// reducedSqueezeRow0 |
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#pragma unroll 8 |
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for (int i = 0; i < 8; i++) |
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{ |
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#pragma unroll 12 |
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for (int j = 0; j<12; j++) { |
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Matrix[j + 84 - 12 * i][0] = state[j]; |
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} |
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round_lyra_sm2(state); |
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} |
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// reducedSqueezeRow1 |
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#pragma unroll 8 |
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for (int i = 0; i < 8; i++) |
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{ |
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#pragma unroll 12 |
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for (int j = 0; j<12; j++) { |
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state[j] ^= Matrix[j + 12 * i][0]; |
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} |
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round_lyra_sm2(state); |
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#pragma unroll 12 |
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for (int j = 0; j<12; j++) { |
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Matrix[j + 84 - 12 * i][1] = Matrix[j + 12 * i][0] ^ state[j]; |
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} |
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} |
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reduceDuplexRowSetup(1, 0, 2, state, Matrix); |
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reduceDuplexRowSetup(2, 1, 3, state, Matrix); |
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reduceDuplexRowSetup(3, 0, 4, state, Matrix); |
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reduceDuplexRowSetup(4, 3, 5, state, Matrix); |
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reduceDuplexRowSetup(5, 2, 6, state, Matrix); |
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reduceDuplexRowSetup(6, 1, 7, state, Matrix); |
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uint32_t rowa; |
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uint32_t prev = 7; |
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uint32_t iterator = 0; |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator + 3) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator - 1) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator + 3) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator - 1) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator + 3) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator - 1) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator + 3) & 7; |
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} |
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for (uint32_t i = 0; i<8; i++) { |
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rowa = state[0].x & 7; |
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reduceDuplexRow(prev, rowa, iterator); |
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prev = iterator; |
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iterator = (iterator - 1) & 7; |
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} |
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absorbblock(rowa); |
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uint32_t nonce = startNounce + thread; |
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if (((uint64_t*)state)[3] <= ((uint64_t*)pTarget)[3]) { |
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atomicMin(&resNonces[1], resNonces[0]); |
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atomicMin(&resNonces[0], nonce); |
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} |
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} //thread |
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} |
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#else |
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__global__ void lyra2Z_gpu_hash_32_sm2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash, uint32_t *resNonces) {} |
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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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//#include "cuda_vector_uint2x4.h" |
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#define Nrow 8 |
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#define Ncol 8 |
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#define memshift 3 |
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#define BUF_COUNT 0 |
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__device__ uint2 *DMatrix; |
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__device__ __forceinline__ |
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void LD4S(uint2 res[3], const int row, const int col, const int thread, const int threads) |
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{ |
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#if BUF_COUNT != 8 |
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extern __shared__ uint2 shared_mem[]; |
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const int s0 = (Ncol * (row - BUF_COUNT) + col) * memshift; |
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#endif |
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#if BUF_COUNT != 0 |
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const int d0 = (memshift *(Ncol * row + col) * threads + thread)*blockDim.x + threadIdx.x; |
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#endif |
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#if BUF_COUNT == 8 |
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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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res[j] = *(DMatrix + d0 + j * threads * blockDim.x); |
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#elif BUF_COUNT == 0 |
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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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res[j] = shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x]; |
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#else |
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if (row < BUF_COUNT) { |
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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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res[j] = *(DMatrix + d0 + j * threads * blockDim.x); |
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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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res[j] = shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x]; |
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} |
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#endif |
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} |
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__device__ __forceinline__ |
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void ST4S(const int row, const int col, const uint2 data[3], const int thread, const int threads) |
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{ |
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#if BUF_COUNT != 8 |
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extern __shared__ uint2 shared_mem[]; |
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const int s0 = (Ncol * (row - BUF_COUNT) + col) * memshift; |
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#endif |
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#if BUF_COUNT != 0 |
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const int d0 = (memshift *(Ncol * row + col) * threads + thread)*blockDim.x + threadIdx.x; |
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#endif |
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#if BUF_COUNT == 8 |
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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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*(DMatrix + d0 + j * threads * blockDim.x) = data[j]; |
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#elif BUF_COUNT == 0 |
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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data[j]; |
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#else |
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if (row < BUF_COUNT) { |
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#pragma unroll |
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for (int j = 0; j < 3; j++) |
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*(DMatrix + d0 + j * threads * blockDim.x) = data[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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shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data[j]; |
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} |
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#endif |
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} |
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#if __CUDA_ARCH__ >= 300 |
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__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c) |
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{ |
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return __shfl(a, b, c); |
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} |
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__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c) |
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{ |
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return make_uint2(__shfl(a.x, b, c), __shfl(a.y, b, c)); |
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} |
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__device__ __forceinline__ |
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void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c) |
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{ |
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a1 = WarpShuffle(a1, b1, c); |
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a2 = WarpShuffle(a2, b2, c); |
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a3 = WarpShuffle(a3, b3, c); |
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} |
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#else |
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__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c) |
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{ |
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extern __shared__ uint2 shared_mem[]; |
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const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x; |
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uint32_t *_ptr = (uint32_t*)shared_mem; |
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__threadfence_block(); |
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uint32_t buf = _ptr[thread]; |
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_ptr[thread] = a; |
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__threadfence_block(); |
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uint32_t result = _ptr[(thread&~(c - 1)) + (b&(c - 1))]; |
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__threadfence_block(); |
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_ptr[thread] = buf; |
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__threadfence_block(); |
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return result; |
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} |
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__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c) |
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{ |
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extern __shared__ uint2 shared_mem[]; |
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const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x; |
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__threadfence_block(); |
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uint2 buf = shared_mem[thread]; |
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shared_mem[thread] = a; |
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__threadfence_block(); |
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uint2 result = shared_mem[(thread&~(c - 1)) + (b&(c - 1))]; |
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__threadfence_block(); |
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shared_mem[thread] = buf; |
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__threadfence_block(); |
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return result; |
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} |
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__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c) |
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{ |
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extern __shared__ uint2 shared_mem[]; |
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|
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const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x; |
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__threadfence_block(); |
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uint2 buf = shared_mem[thread]; |
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shared_mem[thread] = a1; |
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__threadfence_block(); |
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a1 = shared_mem[(thread&~(c - 1)) + (b1&(c - 1))]; |
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__threadfence_block(); |
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shared_mem[thread] = a2; |
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__threadfence_block(); |
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a2 = shared_mem[(thread&~(c - 1)) + (b2&(c - 1))]; |
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__threadfence_block(); |
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shared_mem[thread] = a3; |
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__threadfence_block(); |
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a3 = shared_mem[(thread&~(c - 1)) + (b3&(c - 1))]; |
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|
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__threadfence_block(); |
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shared_mem[thread] = buf; |
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__threadfence_block(); |
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} |
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#endif |
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__device__ __forceinline__ void round_lyra(uint2 s[4]) |
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{ |
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Gfunc(s[0], s[1], s[2], s[3]); |
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WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 1, threadIdx.x + 2, threadIdx.x + 3, 4); |
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Gfunc(s[0], s[1], s[2], s[3]); |
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WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 3, threadIdx.x + 2, threadIdx.x + 1, 4); |
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} |
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|
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static __device__ __forceinline__ |
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void round_lyra(uint2x4* s) |
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{ |
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Gfunc(s[0].x, s[1].x, s[2].x, s[3].x); |
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Gfunc(s[0].y, s[1].y, s[2].y, s[3].y); |
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Gfunc(s[0].z, s[1].z, s[2].z, s[3].z); |
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Gfunc(s[0].w, s[1].w, s[2].w, s[3].w); |
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Gfunc(s[0].x, s[1].y, s[2].z, s[3].w); |
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Gfunc(s[0].y, s[1].z, s[2].w, s[3].x); |
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Gfunc(s[0].z, s[1].w, s[2].x, s[3].y); |
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Gfunc(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(uint2 state[4], uint32_t thread, const uint32_t threads) |
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{ |
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uint2 state1[3]; |
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|
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#if __CUDA_ARCH__ > 500 |
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#pragma unroll |
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#endif |
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for (int i = 0; i < Nrow; i++) |
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{ |
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ST4S(0, Ncol - i - 1, state, thread, threads); |
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|
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round_lyra(state); |
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} |
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|
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#pragma unroll 4 |
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for (int i = 0; i < Nrow; i++) |
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{ |
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LD4S(state1, 0, i, thread, threads); |
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for (int j = 0; j < 3; j++) |
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state[j] ^= state1[j]; |
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|
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round_lyra(state); |
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|
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for (int j = 0; j < 3; j++) |
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state1[j] ^= state[j]; |
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ST4S(1, Ncol - i - 1, state1, thread, threads); |
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} |
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} |
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|
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static __device__ __forceinline__ |
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void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], uint32_t thread, const uint32_t threads) |
|
{ |
|
uint2 state1[3], state2[3]; |
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|
|
#pragma unroll 1 |
|
for (int i = 0; i < Nrow; i++) |
|
{ |
|
LD4S(state1, rowIn, i, thread, threads); |
|
LD4S(state2, rowInOut, i, thread, threads); |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= state1[j] + state2[j]; |
|
|
|
round_lyra(state); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state1[j] ^= state[j]; |
|
|
|
ST4S(rowOut, Ncol - i - 1, state1, thread, threads); |
|
|
|
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る) |
|
uint2 Data0 = state[0]; |
|
uint2 Data1 = state[1]; |
|
uint2 Data2 = state[2]; |
|
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4); |
|
|
|
if (threadIdx.x == 0) |
|
{ |
|
state2[0] ^= Data2; |
|
state2[1] ^= Data0; |
|
state2[2] ^= Data1; |
|
} else { |
|
state2[0] ^= Data0; |
|
state2[1] ^= Data1; |
|
state2[2] ^= Data2; |
|
} |
|
|
|
ST4S(rowInOut, i, state2, thread, threads); |
|
} |
|
} |
|
|
|
static __device__ __forceinline__ |
|
void reduceDuplexRowt(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], const uint32_t thread, const uint32_t threads) |
|
{ |
|
for (int i = 0; i < Nrow; i++) |
|
{ |
|
uint2 state1[3], state2[3]; |
|
|
|
LD4S(state1, rowIn, i, thread, threads); |
|
LD4S(state2, rowInOut, i, thread, threads); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= state1[j] + state2[j]; |
|
|
|
round_lyra(state); |
|
|
|
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る) |
|
uint2 Data0 = state[0]; |
|
uint2 Data1 = state[1]; |
|
uint2 Data2 = state[2]; |
|
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4); |
|
|
|
if (threadIdx.x == 0) |
|
{ |
|
state2[0] ^= Data2; |
|
state2[1] ^= Data0; |
|
state2[2] ^= Data1; |
|
} |
|
else |
|
{ |
|
state2[0] ^= Data0; |
|
state2[1] ^= Data1; |
|
state2[2] ^= Data2; |
|
} |
|
|
|
ST4S(rowInOut, i, state2, thread, threads); |
|
|
|
LD4S(state1, rowOut, i, thread, threads); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state1[j] ^= state[j]; |
|
|
|
ST4S(rowOut, i, state1, thread, threads); |
|
} |
|
} |
|
|
|
#if 0 |
|
static __device__ __forceinline__ |
|
void reduceDuplexRowt_8(const int rowInOut, uint2* state, const uint32_t thread, const uint32_t threads) |
|
{ |
|
uint2 state1[3], state2[3], last[3]; |
|
|
|
LD4S(state1, 2, 0, thread, threads); |
|
LD4S(last, rowInOut, 0, thread, threads); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= state1[j] + last[j]; |
|
|
|
round_lyra(state); |
|
|
|
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る) |
|
uint2 Data0 = state[0]; |
|
uint2 Data1 = state[1]; |
|
uint2 Data2 = state[2]; |
|
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4); |
|
|
|
if (threadIdx.x == 0) |
|
{ |
|
last[0] ^= Data2; |
|
last[1] ^= Data0; |
|
last[2] ^= Data1; |
|
} else { |
|
last[0] ^= Data0; |
|
last[1] ^= Data1; |
|
last[2] ^= Data2; |
|
} |
|
|
|
if (rowInOut == 5) |
|
{ |
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
last[j] ^= state[j]; |
|
} |
|
|
|
for (int i = 1; i < Nrow; i++) |
|
{ |
|
LD4S(state1, 2, i, thread, threads); |
|
LD4S(state2, rowInOut, i, thread, threads); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= state1[j] + state2[j]; |
|
|
|
round_lyra(state); |
|
} |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= last[j]; |
|
} |
|
#endif |
|
|
|
static __device__ __forceinline__ |
|
void reduceDuplexRowt_8_v2(const int rowIn, const int rowOut, const int rowInOut, uint2* state, const uint32_t thread, const uint32_t threads) |
|
{ |
|
uint2 state1[3], state2[3], last[3]; |
|
|
|
LD4S(state1, rowIn, 0, thread, threads); |
|
LD4S(last, rowInOut, 0, thread, threads); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= state1[j] + last[j]; |
|
|
|
round_lyra(state); |
|
|
|
//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る) |
|
uint2 Data0 = state[0]; |
|
uint2 Data1 = state[1]; |
|
uint2 Data2 = state[2]; |
|
WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4); |
|
|
|
if (threadIdx.x == 0) |
|
{ |
|
last[0] ^= Data2; |
|
last[1] ^= Data0; |
|
last[2] ^= Data1; |
|
} |
|
else { |
|
last[0] ^= Data0; |
|
last[1] ^= Data1; |
|
last[2] ^= Data2; |
|
} |
|
|
|
if (rowInOut == rowOut) { |
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
last[j] ^= state[j]; |
|
} |
|
|
|
for (int i = 1; i < Nrow; i++) |
|
{ |
|
LD4S(state1, rowIn, i, thread, threads); |
|
LD4S(state2, rowInOut, i, thread, threads); |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= state1[j] + state2[j]; |
|
|
|
round_lyra(state); |
|
} |
|
|
|
#pragma unroll |
|
for (int j = 0; j < 3; j++) |
|
state[j] ^= last[j]; |
|
} |
|
|
|
__global__ |
|
__launch_bounds__(64, 1) |
|
void lyra2Z_gpu_hash_32_1(uint32_t threads, uint32_t startNounce, uint2 *g_hash) |
|
{ |
|
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); |
|
const uint2x4 Mask[2] = { |
|
0x00000020UL, 0x00000000UL, 0x00000020UL, 0x00000000UL, |
|
0x00000020UL, 0x00000000UL, 0x00000008UL, 0x00000000UL, |
|
0x00000008UL, 0x00000000UL, 0x00000008UL, 0x00000000UL, |
|
0x00000080UL, 0x00000000UL, 0x00000000UL, 0x01000000UL |
|
}; |
|
const uint2x4 blake2b_IV[2] = { |
|
0xf3bcc908lu, 0x6a09e667lu, |
|
0x84caa73blu, 0xbb67ae85lu, |
|
0xfe94f82blu, 0x3c6ef372lu, |
|
0x5f1d36f1lu, 0xa54ff53alu, |
|
0xade682d1lu, 0x510e527flu, |
|
0x2b3e6c1flu, 0x9b05688clu, |
|
0xfb41bd6blu, 0x1f83d9ablu, |
|
0x137e2179lu, 0x5be0cd19lu |
|
}; |
|
if (thread < threads) |
|
{ |
|
uint2x4 state[4]; |
|
|
|
state[0].x = state[1].x = __ldg(&g_hash[thread + threads * 0]); |
|
state[0].y = state[1].y = __ldg(&g_hash[thread + threads * 1]); |
|
state[0].z = state[1].z = __ldg(&g_hash[thread + threads * 2]); |
|
state[0].w = state[1].w = __ldg(&g_hash[thread + threads * 3]); |
|
state[2] = blake2b_IV[0]; |
|
state[3] = blake2b_IV[1]; |
|
|
|
for (int i = 0; i<12; i++) |
|
round_lyra(state); |
|
|
|
state[0] ^= Mask[0]; |
|
state[1] ^= Mask[1]; |
|
|
|
for (int i = 0; i<12; i++) |
|
round_lyra(state); //because 12 is not enough |
|
|
|
((uint2x4*)DMatrix)[threads * 0 + thread] = state[0]; |
|
((uint2x4*)DMatrix)[threads * 1 + thread] = state[1]; |
|
((uint2x4*)DMatrix)[threads * 2 + thread] = state[2]; |
|
((uint2x4*)DMatrix)[threads * 3 + thread] = state[3]; |
|
} |
|
} |
|
|
|
__global__ |
|
__launch_bounds__(TPB52, 1) |
|
void lyra2Z_gpu_hash_32_2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash) |
|
{ |
|
const uint32_t thread = blockDim.y * blockIdx.x + threadIdx.y; |
|
|
|
if (thread < threads) |
|
{ |
|
uint2 state[4]; |
|
state[0] = __ldg(&DMatrix[(0 * threads + thread) * blockDim.x + threadIdx.x]); |
|
state[1] = __ldg(&DMatrix[(1 * threads + thread) * blockDim.x + threadIdx.x]); |
|
state[2] = __ldg(&DMatrix[(2 * threads + thread) * blockDim.x + threadIdx.x]); |
|
state[3] = __ldg(&DMatrix[(3 * threads + thread) * blockDim.x + threadIdx.x]); |
|
|
|
reduceDuplex(state, thread, threads); |
|
reduceDuplexRowSetup(1, 0, 2, state, thread, threads); |
|
reduceDuplexRowSetup(2, 1, 3, state, thread, threads); |
|
reduceDuplexRowSetup(3, 0, 4, state, thread, threads); |
|
reduceDuplexRowSetup(4, 3, 5, state, thread, threads); |
|
reduceDuplexRowSetup(5, 2, 6, state, thread, threads); |
|
reduceDuplexRowSetup(6, 1, 7, state, thread, threads); |
|
|
|
uint32_t rowa; // = WarpShuffle(state[0].x, 0, 4) & 7; |
|
uint32_t prev = 7; |
|
uint32_t iterator = 0; |
|
|
|
//for (uint32_t j=0;j<4;j++) { |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator + 3) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator - 1) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator + 3) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator - 1) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator + 3) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator - 1) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<8; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator + 3) & 7; |
|
} |
|
|
|
for (uint32_t i = 0; i<7; i++) { |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt(prev, rowa, iterator, state, thread, threads); |
|
prev = iterator; |
|
iterator = (iterator - 1) & 7; |
|
} |
|
|
|
//} |
|
rowa = WarpShuffle(state[0].x, 0, 4) & 7; |
|
reduceDuplexRowt_8_v2(prev,iterator,rowa, state, thread, threads); |
|
|
|
DMatrix[(0 * threads + thread) * blockDim.x + threadIdx.x] = state[0]; |
|
DMatrix[(1 * threads + thread) * blockDim.x + threadIdx.x] = state[1]; |
|
DMatrix[(2 * threads + thread) * blockDim.x + threadIdx.x] = state[2]; |
|
DMatrix[(3 * threads + thread) * blockDim.x + threadIdx.x] = state[3]; |
|
} |
|
} |
|
|
|
__global__ |
|
__launch_bounds__(64, 1) |
|
void lyra2Z_gpu_hash_32_3(uint32_t threads, uint32_t startNounce, uint2 *g_hash, uint32_t *resNonces) |
|
{ |
|
const uint32_t thread = blockDim.x * blockIdx.x + threadIdx.x; |
|
|
|
uint28 state[4]; |
|
|
|
if (thread < threads) |
|
{ |
|
state[0] = __ldg4(&((uint2x4*)DMatrix)[threads * 0 + thread]); |
|
state[1] = __ldg4(&((uint2x4*)DMatrix)[threads * 1 + thread]); |
|
state[2] = __ldg4(&((uint2x4*)DMatrix)[threads * 2 + thread]); |
|
state[3] = __ldg4(&((uint2x4*)DMatrix)[threads * 3 + thread]); |
|
|
|
for (int i = 0; i < 12; i++) |
|
round_lyra(state); |
|
|
|
uint32_t nonce = startNounce + thread; |
|
if (((uint64_t*)state)[3] <= ((uint64_t*)pTarget)[3]) { |
|
atomicMin(&resNonces[1], resNonces[0]); |
|
atomicMin(&resNonces[0], nonce); |
|
} |
|
/* |
|
g_hash[thread + threads * 0] = state[0].x; |
|
g_hash[thread + threads * 1] = state[0].y; |
|
g_hash[thread + threads * 2] = state[0].z; |
|
g_hash[thread + threads * 3] = state[0].w; |
|
*/ |
|
} |
|
} |
|
#else |
|
#if __CUDA_ARCH__ < 350 |
|
__device__ void* DMatrix; |
|
#endif |
|
__global__ void lyra2Z_gpu_hash_32_1(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {} |
|
__global__ void lyra2Z_gpu_hash_32_2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash) {} |
|
__global__ void lyra2Z_gpu_hash_32_3(uint32_t threads, uint32_t startNounce, uint2 *g_hash, uint32_t *resNonces) {} |
|
#endif |
|
|
|
__host__ |
|
void lyra2Z_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix) |
|
{ |
|
// just assign the device pointer allocated in main loop |
|
cudaMemcpyToSymbol(DMatrix, &d_matrix, sizeof(uint64_t*), 0, cudaMemcpyHostToDevice); |
|
cudaMalloc(&d_GNonces[thr_id], 2 * sizeof(uint32_t)); |
|
cudaMallocHost(&h_GNonces[thr_id], 2 * sizeof(uint32_t)); |
|
} |
|
|
|
__host__ |
|
void lyra2Z_cpu_init_sm2(int thr_id, uint32_t threads) |
|
{ |
|
// just assign the device pointer allocated in main loop |
|
cudaMalloc(&d_GNonces[thr_id], 2 * sizeof(uint32_t)); |
|
cudaMallocHost(&h_GNonces[thr_id], 2 * sizeof(uint32_t)); |
|
} |
|
|
|
__host__ |
|
void lyra2Z_cpu_free(int thr_id) |
|
{ |
|
cudaFree(d_GNonces[thr_id]); |
|
cudaFreeHost(h_GNonces[thr_id]); |
|
} |
|
|
|
__host__ |
|
uint32_t lyra2Z_getSecNonce(int thr_id, int num) |
|
{ |
|
uint32_t results[2]; |
|
memset(results, 0xFF, sizeof(results)); |
|
cudaMemcpy(results, d_GNonces[thr_id], sizeof(results), cudaMemcpyDeviceToHost); |
|
if (results[1] == results[0]) |
|
return UINT32_MAX; |
|
return results[num]; |
|
} |
|
|
|
__host__ |
|
void lyra2Z_setTarget(const void *pTargetIn) |
|
{ |
|
cudaMemcpyToSymbol(pTarget, pTargetIn, 32, 0, cudaMemcpyHostToDevice); |
|
} |
|
|
|
__host__ |
|
uint32_t lyra2Z_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_hash, bool gtx750ti) |
|
{ |
|
uint32_t result = UINT32_MAX; |
|
cudaMemset(d_GNonces[thr_id], 0xff, 2 * sizeof(uint32_t)); |
|
int dev_id = device_map[thr_id % MAX_GPUS]; |
|
|
|
uint32_t tpb = TPB52; |
|
|
|
if (device_sm[dev_id] == 500) |
|
tpb = TPB50; |
|
if (device_sm[dev_id] == 200) |
|
tpb = TPB20; |
|
|
|
dim3 grid1((threads * 4 + tpb - 1) / tpb); |
|
dim3 block1(4, tpb >> 2); |
|
|
|
dim3 grid2((threads + 64 - 1) / 64); |
|
dim3 block2(64); |
|
|
|
dim3 grid3((threads + tpb - 1) / tpb); |
|
dim3 block3(tpb); |
|
|
|
if (device_sm[dev_id] >= 520) |
|
{ |
|
lyra2Z_gpu_hash_32_1 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash); |
|
|
|
lyra2Z_gpu_hash_32_2 <<< grid1, block1, 24 * (8 - 0) * sizeof(uint2) * tpb >>> (threads, startNounce, d_hash); |
|
|
|
lyra2Z_gpu_hash_32_3 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash, d_GNonces[thr_id]); |
|
} |
|
else if (device_sm[dev_id] == 500 || device_sm[dev_id] == 350) |
|
{ |
|
size_t shared_mem = 0; |
|
|
|
if (gtx750ti) |
|
// 8Warpに調整のため、8192バイト確保する |
|
shared_mem = 8192; |
|
else |
|
// 10Warpに調整のため、6144バイト確保する |
|
shared_mem = 6144; |
|
|
|
lyra2Z_gpu_hash_32_1_sm5 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash); |
|
|
|
lyra2Z_gpu_hash_32_2_sm5 <<< grid1, block1, shared_mem >>> (threads, startNounce, (uint2*)d_hash); |
|
|
|
lyra2Z_gpu_hash_32_3_sm5 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash, d_GNonces[thr_id]); |
|
} |
|
else |
|
lyra2Z_gpu_hash_32_sm2 <<< grid3, block3 >>> (threads, startNounce, d_hash, d_GNonces[thr_id]); |
|
|
|
// get first found nonce |
|
cudaMemcpy(h_GNonces[thr_id], d_GNonces[thr_id], 1 * sizeof(uint32_t), cudaMemcpyDeviceToHost); |
|
result = *h_GNonces[thr_id]; |
|
|
|
return result; |
|
}
|
|
|