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GOSTcoin support for ccminer CUDA miner project, compatible with most nvidia cards
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ccminer/cuda_myriadgroestl.cu

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// Auf Myriadcoin spezialisierte Version von Groestl inkl. Bitslice
#include <stdio.h>
#include <memory.h>
#include "cuda_helper.h"
// aus cpu-miner.c
extern int device_map[8];
// aus heavy.cu
extern cudaError_t MyStreamSynchronize(cudaStream_t stream, int situation, int thr_id);
// diese Struktur wird in der Init Funktion angefordert
static cudaDeviceProp props[8];
// globaler Speicher für alle HeftyHashes aller Threads
__constant__ uint32_t pTarget[8]; // Single GPU
uint32_t *d_outputHashes[8];
extern uint32_t *d_resultNonce[8];
__constant__ uint32_t myriadgroestl_gpu_msg[32];
// muss expandiert werden
__constant__ uint32_t myr_sha256_gpu_constantTable[64];
__constant__ uint32_t myr_sha256_gpu_constantTable2[64];
__constant__ uint32_t myr_sha256_gpu_hashTable[8];
uint32_t myr_sha256_cpu_hashTable[] = {
0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a, 0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19 };
uint32_t myr_sha256_cpu_constantTable[] = {
0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5,
0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174,
0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da,
0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967,
0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85,
0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070,
0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3,
0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2,
};
uint32_t myr_sha256_cpu_w2Table[] = {
0x80000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000,
0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000000, 0x00000200,
0x80000000, 0x01400000, 0x00205000, 0x00005088, 0x22000800, 0x22550014, 0x05089742, 0xa0000020,
0x5a880000, 0x005c9400, 0x0016d49d, 0xfa801f00, 0xd33225d0, 0x11675959, 0xf6e6bfda, 0xb30c1549,
0x08b2b050, 0x9d7c4c27, 0x0ce2a393, 0x88e6e1ea, 0xa52b4335, 0x67a16f49, 0xd732016f, 0x4eeb2e91,
0x5dbf55e5, 0x8eee2335, 0xe2bc5ec2, 0xa83f4394, 0x45ad78f7, 0x36f3d0cd, 0xd99c05e8, 0xb0511dc7,
0x69bc7ac4, 0xbd11375b, 0xe3ba71e5, 0x3b209ff2, 0x18feee17, 0xe25ad9e7, 0x13375046, 0x0515089d,
0x4f0d0f04, 0x2627484e, 0x310128d2, 0xc668b434, 0x420841cc, 0x62d311b8, 0xe59ba771, 0x85a7a484 };
// 64 Register Variante für Compute 3.0
#include "groestl_functions_quad.cu"
#include "bitslice_transformations_quad.cu"
#define SWAB32(x) ( ((x & 0x000000FF) << 24) | ((x & 0x0000FF00) << 8) | ((x & 0x00FF0000) >> 8) | ((x & 0xFF000000) >> 24) )
#if __CUDA_ARCH__ < 350
// Kepler (Compute 3.0)
#define ROTR32(x, n) (((x) >> (n)) | ((x) << (32 - (n))))
#else
// Kepler (Compute 3.5)
#define ROTR32(x, n) __funnelshift_r( (x), (x), (n) )
#endif
#define R(x, n) ((x) >> (n))
#define Ch(x, y, z) ((x & (y ^ z)) ^ z)
#define Maj(x, y, z) ((x & (y | z)) | (y & z))
#define S0(x) (ROTR32(x, 2) ^ ROTR32(x, 13) ^ ROTR32(x, 22))
#define S1(x) (ROTR32(x, 6) ^ ROTR32(x, 11) ^ ROTR32(x, 25))
#define s0(x) (ROTR32(x, 7) ^ ROTR32(x, 18) ^ R(x, 3))
#define s1(x) (ROTR32(x, 17) ^ ROTR32(x, 19) ^ R(x, 10))
__device__ void myriadgroestl_gpu_sha256(uint32_t *message)
{
uint32_t W1[16];
uint32_t W2[16];
// Initialisiere die register a bis h mit der Hash-Tabelle
uint32_t regs[8];
uint32_t hash[8];
// pre
#pragma unroll 8
for (int k=0; k < 8; k++)
{
regs[k] = myr_sha256_gpu_hashTable[k];
hash[k] = regs[k];
}
#pragma unroll 16
for(int k=0;k<16;k++)
W1[k] = SWAB32(message[k]);
// Progress W1
#pragma unroll 16
for(int j=0;j<16;j++)
{
uint32_t T1, T2;
T1 = regs[7] + S1(regs[4]) + Ch(regs[4], regs[5], regs[6]) + myr_sha256_gpu_constantTable[j] + W1[j];
T2 = S0(regs[0]) + Maj(regs[0], regs[1], regs[2]);
#pragma unroll 7
for (int k=6; k >= 0; k--) regs[k+1] = regs[k];
regs[0] = T1 + T2;
regs[4] += T1;
}
// Progress W2...W3
////// PART 1
#pragma unroll 2
for(int j=0;j<2;j++)
W2[j] = s1(W1[14+j]) + W1[9+j] + s0(W1[1+j]) + W1[j];
#pragma unroll 5
for(int j=2;j<7;j++)
W2[j] = s1(W2[j-2]) + W1[9+j] + s0(W1[1+j]) + W1[j];
#pragma unroll 8
for(int j=7;j<15;j++)
W2[j] = s1(W2[j-2]) + W2[j-7] + s0(W1[1+j]) + W1[j];
W2[15] = s1(W2[13]) + W2[8] + s0(W2[0]) + W1[15];
// Rundenfunktion
#pragma unroll 16
for(int j=0;j<16;j++)
{
uint32_t T1, T2;
T1 = regs[7] + S1(regs[4]) + Ch(regs[4], regs[5], regs[6]) + myr_sha256_gpu_constantTable[j + 16] + W2[j];
T2 = S0(regs[0]) + Maj(regs[0], regs[1], regs[2]);
#pragma unroll 7
for (int l=6; l >= 0; l--) regs[l+1] = regs[l];
regs[0] = T1 + T2;
regs[4] += T1;
}
////// PART 2
#pragma unroll 2
for(int j=0;j<2;j++)
W1[j] = s1(W2[14+j]) + W2[9+j] + s0(W2[1+j]) + W2[j];
#pragma unroll 5
for(int j=2;j<7;j++)
W1[j] = s1(W1[j-2]) + W2[9+j] + s0(W2[1+j]) + W2[j];
#pragma unroll 8
for(int j=7;j<15;j++)
W1[j] = s1(W1[j-2]) + W1[j-7] + s0(W2[1+j]) + W2[j];
W1[15] = s1(W1[13]) + W1[8] + s0(W1[0]) + W2[15];
// Rundenfunktion
#pragma unroll 16
for(int j=0;j<16;j++)
{
uint32_t T1, T2;
T1 = regs[7] + S1(regs[4]) + Ch(regs[4], regs[5], regs[6]) + myr_sha256_gpu_constantTable[j + 32] + W1[j];
T2 = S0(regs[0]) + Maj(regs[0], regs[1], regs[2]);
#pragma unroll 7
for (int l=6; l >= 0; l--) regs[l+1] = regs[l];
regs[0] = T1 + T2;
regs[4] += T1;
}
////// PART 3
#pragma unroll 2
for(int j=0;j<2;j++)
W2[j] = s1(W1[14+j]) + W1[9+j] + s0(W1[1+j]) + W1[j];
#pragma unroll 5
for(int j=2;j<7;j++)
W2[j] = s1(W2[j-2]) + W1[9+j] + s0(W1[1+j]) + W1[j];
#pragma unroll 8
for(int j=7;j<15;j++)
W2[j] = s1(W2[j-2]) + W2[j-7] + s0(W1[1+j]) + W1[j];
W2[15] = s1(W2[13]) + W2[8] + s0(W2[0]) + W1[15];
// Rundenfunktion
#pragma unroll 16
for(int j=0;j<16;j++)
{
uint32_t T1, T2;
T1 = regs[7] + S1(regs[4]) + Ch(regs[4], regs[5], regs[6]) + myr_sha256_gpu_constantTable[j + 48] + W2[j];
T2 = S0(regs[0]) + Maj(regs[0], regs[1], regs[2]);
#pragma unroll 7
for (int l=6; l >= 0; l--) regs[l+1] = regs[l];
regs[0] = T1 + T2;
regs[4] += T1;
}
#pragma unroll 8
for(int k=0;k<8;k++)
hash[k] += regs[k];
/////
///// Zweite Runde (wegen Msg-Padding)
/////
#pragma unroll 8
for(int k=0;k<8;k++)
regs[k] = hash[k];
// Progress W1
#pragma unroll 64
for(int j=0;j<64;j++)
{
uint32_t T1, T2;
T1 = regs[7] + S1(regs[4]) + Ch(regs[4], regs[5], regs[6]) + myr_sha256_gpu_constantTable2[j];
T2 = S0(regs[0]) + Maj(regs[0], regs[1], regs[2]);
#pragma unroll 7
for (int k=6; k >= 0; k--) regs[k+1] = regs[k];
regs[0] = T1 + T2;
regs[4] += T1;
}
#pragma unroll 8
for(int k=0;k<8;k++)
hash[k] += regs[k];
//// FERTIG
#pragma unroll 8
for(int k=0;k<8;k++)
message[k] = SWAB32(hash[k]);
}
__global__ void __launch_bounds__(256, 4)
myriadgroestl_gpu_hash_quad(int threads, uint32_t startNounce, uint32_t *hashBuffer)
{
// durch 4 dividieren, weil jeweils 4 Threads zusammen ein Hash berechnen
int thread = (blockDim.x * blockIdx.x + threadIdx.x) / 4;
if (thread < threads)
{
// GROESTL
uint32_t paddedInput[8];
#pragma unroll 8
for(int k=0;k<8;k++) paddedInput[k] = myriadgroestl_gpu_msg[4*k+threadIdx.x%4];
uint32_t nounce = startNounce + thread;
if ((threadIdx.x % 4) == 3)
paddedInput[4] = SWAB32(nounce); // 4*4+3 = 19
uint32_t msgBitsliced[8];
to_bitslice_quad(paddedInput, msgBitsliced);
uint32_t state[8];
groestl512_progressMessage_quad(state, msgBitsliced);
uint32_t out_state[16];
from_bitslice_quad(state, out_state);
if ((threadIdx.x & 0x03) == 0)
{
uint32_t *outpHash = &hashBuffer[16 * thread];
#pragma unroll 16
for(int k=0;k<16;k++) outpHash[k] = out_state[k];
}
}
}
__global__ void
myriadgroestl_gpu_hash_quad2(int threads, uint32_t startNounce, uint32_t *resNounce, uint32_t *hashBuffer)
{
int thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
uint32_t nounce = startNounce + thread;
uint32_t out_state[16];
uint32_t *inpHash = &hashBuffer[16 * thread];
#pragma unroll 16
for (int i=0; i < 16; i++)
out_state[i] = inpHash[i];
myriadgroestl_gpu_sha256(out_state);
int i, position = -1;
bool rc = true;
#pragma unroll 8
for (i = 7; i >= 0; i--) {
if (out_state[i] > pTarget[i]) {
if(position < i) {
position = i;
rc = false;
}
}
if (out_state[i] < pTarget[i]) {
if(position < i) {
position = i;
rc = true;
}
}
}
if(rc == true)
if(resNounce[0] > nounce)
resNounce[0] = nounce;
}
}
// Setup-Funktionen
__host__ void myriadgroestl_cpu_init(int thr_id, int threads)
{
cudaSetDevice(device_map[thr_id]);
cudaMemcpyToSymbol( myr_sha256_gpu_hashTable,
myr_sha256_cpu_hashTable,
sizeof(uint32_t) * 8 );
cudaMemcpyToSymbol( myr_sha256_gpu_constantTable,
myr_sha256_cpu_constantTable,
sizeof(uint32_t) * 64 );
// zweite CPU-Tabelle bauen und auf die GPU laden
uint32_t temp[64];
for(int i=0;i<64;i++)
temp[i] = myr_sha256_cpu_w2Table[i] + myr_sha256_cpu_constantTable[i];
cudaMemcpyToSymbol( myr_sha256_gpu_constantTable2,
temp,
sizeof(uint32_t) * 64 );
cudaGetDeviceProperties(&props[thr_id], device_map[thr_id]);
// Speicher für Gewinner-Nonce belegen
cudaMalloc(&d_resultNonce[thr_id], sizeof(uint32_t));
// Speicher für temporäreHashes
cudaMalloc(&d_outputHashes[thr_id], 16*sizeof(uint32_t)*threads);
}
__host__ void myriadgroestl_cpu_setBlock(int thr_id, void *data, void *pTargetIn)
{
// Nachricht expandieren und setzen
uint32_t msgBlock[32];
memset(msgBlock, 0, sizeof(uint32_t) * 32);
memcpy(&msgBlock[0], data, 80);
// Erweitere die Nachricht auf den Nachrichtenblock (padding)
// Unsere Nachricht hat 80 Byte
msgBlock[20] = 0x80;
msgBlock[31] = 0x01000000;
// groestl512 braucht hierfür keinen CPU-Code (die einzige Runde wird
// auf der GPU ausgeführt)
// Blockheader setzen (korrekte Nonce und Hefty Hash fehlen da drin noch)
cudaMemcpyToSymbol( myriadgroestl_gpu_msg,
msgBlock,
128);
cudaMemset(d_resultNonce[thr_id], 0xFF, sizeof(uint32_t));
cudaMemcpyToSymbol( pTarget,
pTargetIn,
sizeof(uint32_t) * 8 );
}
__host__ void myriadgroestl_cpu_hash(int thr_id, int threads, uint32_t startNounce, void *outputHashes, uint32_t *nounce)
{
int threadsperblock = 256;
// Compute 3.0 benutzt die registeroptimierte Quad Variante mit Warp Shuffle
// mit den Quad Funktionen brauchen wir jetzt 4 threads pro Hash, daher Faktor 4 bei der Blockzahl
const int factor=4;
// Größe des dynamischen Shared Memory Bereichs
size_t shared_size = 0;
cudaMemset(d_resultNonce[thr_id], 0xFF, sizeof(uint32_t));
// berechne wie viele Thread Blocks wir brauchen
dim3 grid(factor*((threads + threadsperblock-1)/threadsperblock));
dim3 block(threadsperblock);
myriadgroestl_gpu_hash_quad<<<grid, block, shared_size>>>(threads, startNounce, d_outputHashes[thr_id]);
dim3 grid2((threads + threadsperblock-1)/threadsperblock);
myriadgroestl_gpu_hash_quad2<<<grid2, block, shared_size>>>(threads, startNounce, d_resultNonce[thr_id], d_outputHashes[thr_id]);
// Strategisches Sleep Kommando zur Senkung der CPU Last
MyStreamSynchronize(NULL, 0, thr_id);
cudaMemcpy(nounce, d_resultNonce[thr_id], sizeof(uint32_t), cudaMemcpyDeviceToHost);
}