#include <stdio.h> #include <memory.h> #include "cuda_helper.h" // globaler Speicher für alle HeftyHashes aller Threads extern uint32_t *heavy_heftyHashes[MAX_GPUS]; extern uint32_t *heavy_nonceVector[MAX_GPUS]; // globaler Speicher für unsere Ergebnisse uint32_t *d_hash5output[MAX_GPUS]; // die Message (112 bzw. 116 Bytes) mit Padding zur Berechnung auf der GPU __constant__ uint64_t c_PaddedMessage[16]; // padded message (80/84+32 bytes + padding) // ---------------------------- BEGIN CUDA blake512 functions ------------------------------------ __constant__ uint8_t c_sigma[16][16]; const uint8_t host_sigma[16][16] = { { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }, {14, 10, 4, 8, 9, 15, 13, 6, 1, 12, 0, 2, 11, 7, 5, 3 }, {11, 8, 12, 0, 5, 2, 15, 13, 10, 14, 3, 6, 7, 1, 9, 4 }, { 7, 9, 3, 1, 13, 12, 11, 14, 2, 6, 5, 10, 4, 0, 15, 8 }, { 9, 0, 5, 7, 2, 4, 10, 15, 14, 1, 11, 12, 6, 8, 3, 13 }, { 2, 12, 6, 10, 0, 11, 8, 3, 4, 13, 7, 5, 15, 14, 1, 9 }, {12, 5, 1, 15, 14, 13, 4, 10, 0, 7, 6, 3, 9, 2, 8, 11 }, {13, 11, 7, 14, 12, 1, 3, 9, 5, 0, 15, 4, 8, 6, 2, 10 }, { 6, 15, 14, 9, 11, 3, 0, 8, 12, 2, 13, 7, 1, 4, 10, 5 }, {10, 2, 8, 4, 7, 6, 1, 5, 15, 11, 9, 14, 3, 12, 13 , 0 }, { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }, {14, 10, 4, 8, 9, 15, 13, 6, 1, 12, 0, 2, 11, 7, 5, 3 }, {11, 8, 12, 0, 5, 2, 15, 13, 10, 14, 3, 6, 7, 1, 9, 4 }, { 7, 9, 3, 1, 13, 12, 11, 14, 2, 6, 5, 10, 4, 0, 15, 8 }, { 9, 0, 5, 7, 2, 4, 10, 15, 14, 1, 11, 12, 6, 8, 3, 13 }, { 2, 12, 6, 10, 0, 11, 8, 3, 4, 13, 7, 5, 15, 14, 1, 9 } }; /* in cuda_helper */ #define SWAP32(x) cuda_swab32(x) #define SWAP64(x) cuda_swab64(x) __constant__ uint64_t c_SecondRound[15]; const uint64_t host_SecondRound[15] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,SWAP64(1),0 }; __constant__ uint64_t c_u512[16]; const uint64_t host_u512[16] = { 0x243f6a8885a308d3ULL, 0x13198a2e03707344ULL, 0xa4093822299f31d0ULL, 0x082efa98ec4e6c89ULL, 0x452821e638d01377ULL, 0xbe5466cf34e90c6cULL, 0xc0ac29b7c97c50ddULL, 0x3f84d5b5b5470917ULL, 0x9216d5d98979fb1bULL, 0xd1310ba698dfb5acULL, 0x2ffd72dbd01adfb7ULL, 0xb8e1afed6a267e96ULL, 0xba7c9045f12c7f99ULL, 0x24a19947b3916cf7ULL, 0x0801f2e2858efc16ULL, 0x636920d871574e69ULL }; #define G(a,b,c,d,e) \ v[a] += (m[sigma[i][e]] ^ u512[sigma[i][e+1]]) + v[b];\ v[d] = SWAPDWORDS( v[d] ^ v[a]); \ v[c] += v[d]; \ v[b] = ROTR64( v[b] ^ v[c],25); \ v[a] += (m[sigma[i][e+1]] ^ u512[sigma[i][e]])+v[b]; \ v[d] = ROTR64( v[d] ^ v[a],16); \ v[c] += v[d]; \ v[b] = ROTR64( v[b] ^ v[c],11); template <int BLOCKSIZE> __device__ void blake512_compress( uint64_t *h, const uint64_t *block, int nullt, const uint8_t ((*sigma)[16]), const uint64_t *u512 ) { uint64_t v[16], m[16], i; #pragma unroll 16 for( i = 0; i < 16; ++i ) m[i] = cuda_swab64(block[i]); #pragma unroll 8 for( i = 0; i < 8; ++i ) v[i] = h[i]; v[ 8] = u512[0]; v[ 9] = u512[1]; v[10] = u512[2]; v[11] = u512[3]; v[12] = u512[4]; v[13] = u512[5]; v[14] = u512[6]; v[15] = u512[7]; /* don't xor t when the block is only padding */ if ( !nullt ) { v[12] ^= 8*(BLOCKSIZE+32); v[13] ^= 8*(BLOCKSIZE+32); } //#pragma unroll 16 for( i = 0; i < 16; ++i ) { /* column step */ G( 0, 4, 8, 12, 0 ); G( 1, 5, 9, 13, 2 ); G( 2, 6, 10, 14, 4 ); G( 3, 7, 11, 15, 6 ); /* diagonal step */ G( 0, 5, 10, 15, 8 ); G( 1, 6, 11, 12, 10 ); G( 2, 7, 8, 13, 12 ); G( 3, 4, 9, 14, 14 ); } #pragma unroll 16 for( i = 0; i < 16; ++i ) h[i % 8] ^= v[i]; } template <int BLOCKSIZE> __global__ void blake512_gpu_hash(uint32_t threads, uint32_t startNounce, void *outputHash, uint32_t *heftyHashes, uint32_t *nonceVector) { uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); if (thread < threads) { // bestimme den aktuellen Zähler //uint32_t nounce = startNounce + thread; uint32_t nounce = nonceVector[thread]; // Index-Position des Hashes in den Hash Puffern bestimmen (Hefty1 und outputHash) uint32_t hashPosition = nounce - startNounce; // State vorbereiten uint64_t h[8]; h[0] = 0x6a09e667f3bcc908ULL; h[1] = 0xbb67ae8584caa73bULL; h[2] = 0x3c6ef372fe94f82bULL; h[3] = 0xa54ff53a5f1d36f1ULL; h[4] = 0x510e527fade682d1ULL; h[5] = 0x9b05688c2b3e6c1fULL; h[6] = 0x1f83d9abfb41bd6bULL; h[7] = 0x5be0cd19137e2179ULL; // 128 Byte für die Message uint64_t buf[16]; // Message für die erste Runde in Register holen #pragma unroll 16 for (int i=0; i < 16; ++i) buf[i] = c_PaddedMessage[i]; // die Nounce durch die thread-spezifische ersetzen buf[9] = REPLACE_HIDWORD(buf[9], nounce); uint32_t *hefty = heftyHashes + 8 * hashPosition; if (BLOCKSIZE == 84) { // den thread-spezifischen Hefty1 hash einsetzen // aufwändig, weil das nicht mit uint64_t Wörtern aligned ist. buf[10] = REPLACE_HIDWORD(buf[10], hefty[0]); buf[11] = REPLACE_LODWORD(buf[11], hefty[1]); buf[11] = REPLACE_HIDWORD(buf[11], hefty[2]); buf[12] = REPLACE_LODWORD(buf[12], hefty[3]); buf[12] = REPLACE_HIDWORD(buf[12], hefty[4]); buf[13] = REPLACE_LODWORD(buf[13], hefty[5]); buf[13] = REPLACE_HIDWORD(buf[13], hefty[6]); buf[14] = REPLACE_LODWORD(buf[14], hefty[7]); } else if (BLOCKSIZE == 80) { buf[10] = MAKE_ULONGLONG(hefty[0], hefty[1]); buf[11] = MAKE_ULONGLONG(hefty[2], hefty[3]); buf[12] = MAKE_ULONGLONG(hefty[4], hefty[5]); buf[13] = MAKE_ULONGLONG(hefty[6], hefty[7]); } // erste Runde blake512_compress<BLOCKSIZE>( h, buf, 0, c_sigma, c_u512 ); // zweite Runde #pragma unroll 15 for (int i=0; i < 15; ++i) buf[i] = c_SecondRound[i]; buf[15] = SWAP64(8*(BLOCKSIZE+32)); // Blocksize in Bits einsetzen blake512_compress<BLOCKSIZE>( h, buf, 1, c_sigma, c_u512 ); // Hash rauslassen uint64_t *outHash = (uint64_t *)outputHash + 8 * hashPosition; #pragma unroll 8 for (int i=0; i < 8; ++i) outHash[i] = cuda_swab64( h[i] ); } } // ---------------------------- END CUDA blake512 functions ------------------------------------ // Setup Function __host__ void blake512_cpu_init(int thr_id, uint32_t threads) { // Kopiere die Hash-Tabellen in den GPU-Speicher cudaMemcpyToSymbol( c_sigma, host_sigma, sizeof(host_sigma), 0, cudaMemcpyHostToDevice); cudaMemcpyToSymbol( c_u512, host_u512, sizeof(host_u512), 0, cudaMemcpyHostToDevice); cudaMemcpyToSymbol( c_SecondRound, host_SecondRound, sizeof(host_SecondRound), 0, cudaMemcpyHostToDevice); // Speicher für alle Ergebnisse belegen CUDA_SAFE_CALL(cudaMalloc(&d_hash5output[thr_id], (size_t) 64 * threads)); } __host__ void blake512_cpu_free(int thr_id) { cudaFree(d_hash5output[thr_id]); } static int BLOCKSIZE = 84; __host__ void blake512_cpu_setBlock(void *pdata, int len) // data muss 84-Byte haben! // heftyHash hat 32-Byte { unsigned char PaddedMessage[128]; if (len == 84) { // Message mit Padding für erste Runde bereitstellen memcpy(PaddedMessage, pdata, 84); memset(PaddedMessage+84, 0, 32); // leeres Hefty Hash einfüllen memset(PaddedMessage+116, 0, 12); PaddedMessage[116] = 0x80; } else if (len == 80) { memcpy(PaddedMessage, pdata, 80); memset(PaddedMessage+80, 0, 32); // leeres Hefty Hash einfüllen memset(PaddedMessage+112, 0, 16); PaddedMessage[112] = 0x80; } // die Message (116 Bytes) ohne Padding zur Berechnung auf der GPU cudaMemcpyToSymbol( c_PaddedMessage, PaddedMessage, 16*sizeof(uint64_t), 0, cudaMemcpyHostToDevice); BLOCKSIZE = len; } __host__ void blake512_cpu_hash(int thr_id, uint32_t threads, uint32_t startNounce) { const uint32_t threadsperblock = 256; // berechne wie viele Thread Blocks wir brauchen dim3 grid((threads + threadsperblock-1)/threadsperblock); dim3 block(threadsperblock); // Größe des dynamischen Shared Memory Bereichs size_t shared_size = 0; if (BLOCKSIZE == 80) blake512_gpu_hash<80><<<grid, block, shared_size>>>(threads, startNounce, d_hash5output[thr_id], heavy_heftyHashes[thr_id], heavy_nonceVector[thr_id]); else if (BLOCKSIZE == 84) blake512_gpu_hash<84><<<grid, block, shared_size>>>(threads, startNounce, d_hash5output[thr_id], heavy_heftyHashes[thr_id], heavy_nonceVector[thr_id]); }