GOSTCoin CUDA miner project, compatible with most nvidia cards, containing only gostd algo
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/**
* Lyra2 (v1) cuda implementation based on djm34 work - SM 5/5.2
* tpruvot@github 2015
*/
#include <stdio.h>
#include <memory.h>
#define TPB50 16
#define TPB52 8
#include "cuda_lyra2_sm2.cuh"
#ifdef __INTELLISENSE__
/* just for vstudio code colors */
#define __CUDA_ARCH__ 500
#endif
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 500
#include "cuda_lyra2_vectors.h"
#define uint2x4 uint28
#define memshift 3
#define Ncol 8
#define NcolMask 0x7
__device__ uint2x4* DMatrix;
static __device__ __forceinline__
void Gfunc(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
{
a += b; d ^= a; d = SWAPUINT2(d);
c += d; b ^= c; b = ROR2(b, 24);
a += b; d ^= a; d = ROR2(d, 16);
c += d; b ^= c; b = ROR2(b, 63);
}
static __device__ __forceinline__
void round_lyra(uint2x4* s)
{
Gfunc(s[0].x, s[1].x, s[2].x, s[3].x);
Gfunc(s[0].y, s[1].y, s[2].y, s[3].y);
Gfunc(s[0].z, s[1].z, s[2].z, s[3].z);
Gfunc(s[0].w, s[1].w, s[2].w, s[3].w);
Gfunc(s[0].x, s[1].y, s[2].z, s[3].w);
Gfunc(s[0].y, s[1].z, s[2].w, s[3].x);
Gfunc(s[0].z, s[1].w, s[2].x, s[3].y);
Gfunc(s[0].w, s[1].x, s[2].y, s[3].z);
}
static __device__ __forceinline__
void reduceDuplex(uint2x4 state[4], uint32_t thread)
{
uint2x4 state1[3];
const uint32_t ps1 = (256 * thread);
const uint32_t ps2 = (memshift * 7 + memshift * 8 + 256 * thread);
#pragma unroll 4
for (int i = 0; i < 8; i++)
{
const uint32_t s1 = ps1 + i*memshift;
const uint32_t s2 = ps2 - i*memshift;
for (int j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix+s1)[j]);
for (int j = 0; j < 3; j++)
state[j] ^= state1[j];
round_lyra(state);
for (int j = 0; j < 3; j++)
state1[j] ^= state[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state1[j];
}
}
static __device__ __forceinline__
void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2x4 state[4], uint32_t thread)
{
uint2x4 state1[3], state2[3];
const uint32_t ps1 = ( memshift*8 * rowIn + 256 * thread);
const uint32_t ps2 = ( memshift*8 * rowInOut + 256 * thread);
const uint32_t ps3 = (memshift*7 + memshift*8 * rowOut + 256 * thread);
#pragma unroll 1
for (int i = 0; i < 8; i++)
{
const uint32_t s1 = ps1 + i*memshift;
const uint32_t s2 = ps2 + i*memshift;
for (int j = 0; j < 3; j++)
state1[j]= __ldg4(&(DMatrix + s1)[j]);
for (int j = 0; j < 3; j++)
state2[j]= __ldg4(&(DMatrix + s2)[j]);
for (int j = 0; j < 3; j++) {
uint2x4 tmp = state1[j] + state2[j];
state[j] ^= tmp;
}
round_lyra(state);
for (int j = 0; j < 3; j++) {
const uint32_t s3 = ps3 - i*memshift;
state1[j] ^= state[j];
(DMatrix + s3)[j] = state1[j];
}
((uint2*)state2)[0] ^= ((uint2*)state)[11];
for (int j = 0; j < 11; j++)
((uint2*)state2)[j+1] ^= ((uint2*)state)[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j];
}
}
static __device__ __forceinline__
void reduceDuplexRowt(const int rowIn, const int rowInOut, const int rowOut, uint2x4* state, const uint32_t thread)
{
const uint32_t ps1 = (memshift * 8 * rowIn + 256 * thread);
const uint32_t ps2 = (memshift * 8 * rowInOut + 256 * thread);
const uint32_t ps3 = (memshift * 8 * rowOut + 256 * thread);
#pragma unroll 1
for (int i = 0; i < 8; i++)
{
uint2x4 state1[3], state2[3];
const uint32_t s1 = ps1 + i*memshift;
const uint32_t s2 = ps2 + i*memshift;
for (int j = 0; j < 3; j++) {
state1[j] = __ldg4(&(DMatrix + s1)[j]);
state2[j] = __ldg4(&(DMatrix + s2)[j]);
}
#pragma unroll
for (int j = 0; j < 3; j++) {
state1[j] += state2[j];
state[j] ^= state1[j];
}
round_lyra(state);
((uint2*)state2)[0] ^= ((uint2*)state)[11];
for (int j = 0; j < 11; j++)
((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
if (rowInOut == rowOut) {
for (int j = 0; j < 3; j++) {
state2[j] ^= state[j];
(DMatrix + s2)[j]=state2[j];
}
} else {
const uint32_t s3 = ps3 + i*memshift;
for (int j = 0; j < 3; j++) {
(DMatrix + s2)[j] = state2[j];
(DMatrix + s3)[j] ^= state[j];
}
}
}
}
#if __CUDA_ARCH__ == 500
__global__ __launch_bounds__(TPB50, 1)
#else
__global__ __launch_bounds__(TPB52, 2)
#endif
void lyra2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
{
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
const uint2x4 blake2b_IV[2] = {
{{ 0xf3bcc908, 0x6a09e667 }, { 0x84caa73b, 0xbb67ae85 }, { 0xfe94f82b, 0x3c6ef372 }, { 0x5f1d36f1, 0xa54ff53a }},
{{ 0xade682d1, 0x510e527f }, { 0x2b3e6c1f, 0x9b05688c }, { 0xfb41bd6b, 0x1f83d9ab }, { 0x137e2179, 0x5be0cd19 }}
};
if (thread < threads)
{
uint2x4 state[4];
((uint2*)state)[0] = __ldg(&g_hash[thread]);
((uint2*)state)[1] = __ldg(&g_hash[thread + threads]);
((uint2*)state)[2] = __ldg(&g_hash[thread + threads*2]);
((uint2*)state)[3] = __ldg(&g_hash[thread + threads*3]);
state[1] = state[0];
state[2] = blake2b_IV[0];
state[3] = blake2b_IV[1];
for (int i = 0; i<24; i++)
round_lyra(state); //because 12 is not enough
const uint32_t ps1 = (memshift * 7 + 256 * thread);
for (int i = 0; i < 8; i++)
{
const uint32_t s1 = ps1 - memshift * i;
for (int j = 0; j < 3; j++)
(DMatrix + s1)[j] = (state)[j];
round_lyra(state);
}
reduceDuplex(state, thread);
reduceDuplexRowSetup(1, 0, 2, state, thread);
reduceDuplexRowSetup(2, 1, 3, state, thread);
reduceDuplexRowSetup(3, 0, 4, state, thread);
reduceDuplexRowSetup(4, 3, 5, state, thread);
reduceDuplexRowSetup(5, 2, 6, state, thread);
reduceDuplexRowSetup(6, 1, 7, state, thread);
uint32_t rowa = state[0].x.x & 7;
reduceDuplexRowt(7, rowa, 0, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(0, rowa, 3, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(3, rowa, 6, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(6, rowa, 1, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(1, rowa, 4, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(4, rowa, 7, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(7, rowa, 2, state, thread);
rowa = state[0].x.x & 7;
reduceDuplexRowt(2, rowa, 5, state, thread);
const int32_t shift = (memshift * 8 * rowa + 256 * thread);
#pragma unroll
for (int j = 0; j < 3; j++)
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
for (int i = 0; i < 12; i++)
round_lyra(state);
g_hash[thread] = ((uint2*)state)[0];
g_hash[thread + threads] = ((uint2*)state)[1];
g_hash[thread + threads*2] = ((uint2*)state)[2];
g_hash[thread + threads*3] = ((uint2*)state)[3];
}
}
#else
/* for unsupported SM arch */
__device__ void* DMatrix;
__global__ void lyra2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
#endif
__host__
void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t* d_matrix)
{
cuda_get_arch(thr_id);
cudaMemcpyToSymbol(DMatrix, &d_matrix, sizeof(uint64_t*), 0, cudaMemcpyHostToDevice);
}
__host__
void lyra2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_hash, int order)
{
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] == 350) tpb = TPB30; // to enhance (or not)
if (device_sm[dev_id] <= 300) tpb = TPB30;
dim3 grid((threads + tpb - 1) / tpb);
dim3 block(tpb);
if (device_sm[dev_id] >= 500)
lyra2_gpu_hash_32 <<< grid, block >>> (threads, startNounce, (uint2*)d_hash);
else
lyra2_gpu_hash_32_sm2 <<< grid, block >>> (threads, startNounce, d_hash);
}