GOSTcoin support for ccminer CUDA miner project, compatible with most nvidia cards
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#include <stdio.h>
#include <memory.h>
#include "cuda_lyra2_vectors.h"
#define TPB 8
//
#if __CUDA_ARCH__ < 500
#define vectype ulonglong4
#define u64type uint64_t
#define memshift 4
#elif __CUDA_ARCH__ == 500
#define u64type uint2
#define vectype uint28
#define memshift 3
#else
#define u64type uint2
#define vectype uint28
#define memshift 4
#endif
__device__ vectype *DMatrix;
#ifdef __CUDA_ARCH__
static __device__ __forceinline__
void Gfunc_v35(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
{
a += b; d ^= a; d = SWAPUINT2(d);
c += d; b ^= c; b = ROR24(b);
a += b; d ^= a; d = ROR16(d);
c += d; b ^= c; b = ROR2(b, 63);
}
#if __CUDA_ARCH__ < 500
static __device__ __forceinline__
void Gfunc_v35(unsigned long long &a, unsigned long long &b, unsigned long long &c, unsigned long long &d)
{
a += b; d ^= a; d = ROTR64(d, 32);
c += d; b ^= c; b = ROTR64(b, 24);
a += b; d ^= a; d = ROTR64(d, 16);
c += d; b ^= c; b = ROTR64(b, 63);
}
#endif
static __device__ __forceinline__
void round_lyra_v35(vectype* s)
{
Gfunc_v35(s[0].x, s[1].x, s[2].x, s[3].x);
Gfunc_v35(s[0].y, s[1].y, s[2].y, s[3].y);
Gfunc_v35(s[0].z, s[1].z, s[2].z, s[3].z);
Gfunc_v35(s[0].w, s[1].w, s[2].w, s[3].w);
Gfunc_v35(s[0].x, s[1].y, s[2].z, s[3].w);
Gfunc_v35(s[0].y, s[1].z, s[2].w, s[3].x);
Gfunc_v35(s[0].z, s[1].w, s[2].x, s[3].y);
Gfunc_v35(s[0].w, s[1].x, s[2].y, s[3].z);
}
#else
#define round_lyra_v35(s) {}
#endif
static __device__ __forceinline__
void reduceDuplex(vectype state[4], uint32_t thread)
{
vectype state1[3];
uint32_t ps1 = (256 * thread);
uint32_t ps2 = (memshift * 7 + memshift * 8 + 256 * thread);
#pragma unroll 4
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 + i*memshift;
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_v35(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 reduceDuplexV3(vectype state[4], uint32_t thread)
{
vectype state1[3];
uint32_t ps1 = (256 * thread);
// colomn row
uint32_t ps2 = (memshift * 7 * 8 + memshift * 1 + 64 * memshift * thread);
#pragma unroll 4
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 + 8 * i *memshift;
uint32_t s2 = ps2 - 8 * 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_v35(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 reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
{
vectype state2[3],state1[3];
uint32_t ps1 = ( memshift * 8 * rowIn + 256 * thread);
uint32_t ps2 = ( memshift * 8 * rowInOut + 256 * thread);
uint32_t ps3 = (memshift*7 + memshift * 8 * rowOut + 256 * thread);
#pragma unroll 1
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 + i*memshift;
uint32_t s2 = ps2 + i*memshift;
uint32_t s3 = ps3 - 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++) {
vectype tmp = state1[j] + state2[j];
state[j] ^= tmp;
}
round_lyra_v35(state);
for (int j = 0; j < 3; j++) {
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 reduceDuplexRowSetupV3(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
{
vectype state2[3], state1[3];
uint32_t ps1 = ( memshift * rowIn + 64 * memshift * thread);
uint32_t ps2 = (memshift * rowInOut + 64 * memshift* thread);
uint32_t ps3 = (8 * memshift * 7 + memshift * rowOut + 64 * memshift * thread);
/*
uint32_t ps1 = (256 * thread);
uint32_t ps2 = (256 * thread);
uint32_t ps3 = (256 * thread);
*/
#pragma nounroll
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 + 8*i*memshift;
uint32_t s2 = ps2 + 8*i*memshift;
uint32_t s3 = ps3 - 8*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++) {
vectype tmp = state1[j] + state2[j];
state[j] ^= tmp;
}
round_lyra_v35(state);
for (int j = 0; j < 3; j++) {
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 reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
{
vectype state1[3], state2[3];
uint32_t ps1 = (memshift * 8 * rowIn + 256 * thread);
uint32_t ps2 = (memshift * 8 * rowInOut + 256 * thread);
uint32_t ps3 = (memshift * 8 * rowOut + 256 * thread);
#pragma unroll 1
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 + i*memshift;
uint32_t s2 = ps2 + i*memshift;
uint32_t s3 = ps3 + 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++)
state1[j] += state2[j];
for (int j = 0; j < 3; j++)
state[j] ^= state1[j];
round_lyra_v35(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++)
(DMatrix + s2)[j] = state2[j];
for (int j = 0; j < 3; j++)
(DMatrix + s3)[j] ^= state[j];
} else {
for (int j = 0; j < 3; j++)
state2[j] ^= state[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j]=state2[j];
}
}
}
static __device__ __forceinline__
void reduceDuplexRowtV3(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
{
vectype state1[3], state2[3];
uint32_t ps1 = (memshift * rowIn + 64 * memshift * thread);
uint32_t ps2 = (memshift * rowInOut + 64 * memshift * thread);
uint32_t ps3 = (memshift * rowOut + 64 *memshift * thread);
#pragma nounroll
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 + 8 * i*memshift;
uint32_t s2 = ps2 + 8 * i*memshift;
uint32_t s3 = ps3 + 8 * 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++)
state1[j] += state2[j];
for (int j = 0; j < 3; j++)
state[j] ^= state1[j];
round_lyra_v35(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++)
(DMatrix + s2)[j] = state2[j];
for (int j = 0; j < 3; j++)
(DMatrix + s3)[j] ^= state[j];
}
else {
for (int j = 0; j < 3; j++)
state2[j] ^= state[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j];
}
}
}
#if __CUDA_ARCH__ < 500
__global__ __launch_bounds__(48, 1)
#elif __CUDA_ARCH__ == 500
__global__ __launch_bounds__(16, 1)
#else
__global__ __launch_bounds__(TPB, 1)
#endif
void lyra2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
vectype state[4];
#if __CUDA_ARCH__ > 350
const uint28 blake2b_IV[2] = {
{{ 0xf3bcc908, 0x6a09e667 }, { 0x84caa73b, 0xbb67ae85 }, { 0xfe94f82b, 0x3c6ef372 }, { 0x5f1d36f1, 0xa54ff53a }},
{{ 0xade682d1, 0x510e527f }, { 0x2b3e6c1f, 0x9b05688c }, { 0xfb41bd6b, 0x1f83d9ab }, { 0x137e2179, 0x5be0cd19 }}
};
#else
const ulonglong4 blake2b_IV[2] = {
{ 0x6a09e667f3bcc908, 0xbb67ae8584caa73b, 0x3c6ef372fe94f82b, 0xa54ff53a5f1d36f1 },
{ 0x510e527fade682d1, 0x9b05688c2b3e6c1f, 0x1f83d9abfb41bd6b, 0x5be0cd19137e2179 }
};
#endif
#if __CUDA_ARCH__ == 350
if (thread < threads)
#endif
{
((uint2*)state)[0] = __ldg(&outputHash[thread]);
((uint2*)state)[1] = __ldg(&outputHash[thread + threads]);
((uint2*)state)[2] = __ldg(&outputHash[thread + 2 * threads]);
((uint2*)state)[3] = __ldg(&outputHash[thread + 3 * threads]);
// state[0] = __ldg4(&((vectype*)outputHash)[thread]);
state[1] = state[0];
state[2] = ((vectype*)blake2b_IV)[0];
state[3] = ((vectype*)blake2b_IV)[1];
for (int i = 0; i<24; i++) { //because 12 is not enough
round_lyra_v35(state);
}
uint32_t ps1 = (memshift * 7 + 256 * thread);
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 - memshift * i;
for (int j = 0; j < 3; j++)
(DMatrix + s1)[j] = (state)[j];
round_lyra_v35(state);
}
reduceDuplex(state, thread);
reduceDuplexRowSetupV2(1, 0, 2, state, thread);
reduceDuplexRowSetupV2(2, 1, 3, state, thread);
reduceDuplexRowSetupV2(3, 0, 4, state, thread);
reduceDuplexRowSetupV2(4, 3, 5, state, thread);
reduceDuplexRowSetupV2(5, 2, 6, state, thread);
reduceDuplexRowSetupV2(6, 1, 7, state, thread);
uint32_t rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(7, rowa, 0, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(0, rowa, 3, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(3, rowa, 6, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(6, rowa, 1, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(1, rowa, 4, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(4, rowa, 7, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(7, rowa, 2, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV2(2, rowa, 5, state, thread);
uint32_t shift = (memshift * 8 * rowa + 256 * thread);
for (int j = 0; j < 3; j++)
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
for (int i = 0; i < 12; i++)
round_lyra_v35(state);
outputHash[thread]= ((uint2*)state)[0];
outputHash[thread + threads] = ((uint2*)state)[1];
outputHash[thread + 2 * threads] = ((uint2*)state)[2];
outputHash[thread + 3 * threads] = ((uint2*)state)[3];
// ((vectype*)outputHash)[thread] = state[0];
} //thread
}
#if __CUDA_ARCH__ < 500
__global__ __launch_bounds__(48, 1)
#elif __CUDA_ARCH__ == 500
__global__ __launch_bounds__(16, 1)
#else
__global__ __launch_bounds__(TPB, 1)
#endif
void lyra2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
vectype state[4];
#if __CUDA_ARCH__ > 350
const uint28 blake2b_IV[2] = {
{ { 0xf3bcc908, 0x6a09e667 }, { 0x84caa73b, 0xbb67ae85 }, { 0xfe94f82b, 0x3c6ef372 }, { 0x5f1d36f1, 0xa54ff53a } },
{ { 0xade682d1, 0x510e527f }, { 0x2b3e6c1f, 0x9b05688c }, { 0xfb41bd6b, 0x1f83d9ab }, { 0x137e2179, 0x5be0cd19 } }
};
#else
const ulonglong4 blake2b_IV[2] = {
{ 0x6a09e667f3bcc908, 0xbb67ae8584caa73b, 0x3c6ef372fe94f82b, 0xa54ff53a5f1d36f1 },
{ 0x510e527fade682d1, 0x9b05688c2b3e6c1f, 0x1f83d9abfb41bd6b, 0x5be0cd19137e2179 }
};
#endif
#if __CUDA_ARCH__ == 350
if (thread < threads)
#endif
{
((uint2*)state)[0] = __ldg(&outputHash[thread]);
((uint2*)state)[1] = __ldg(&outputHash[thread + threads]);
((uint2*)state)[2] = __ldg(&outputHash[thread + 2 * threads]);
((uint2*)state)[3] = __ldg(&outputHash[thread + 3 * threads]);
state[1] = state[0];
state[2] = ((vectype*)blake2b_IV)[0];
state[3] = ((vectype*)blake2b_IV)[1];
for (int i = 0; i<24; i++)
round_lyra_v35(state); //because 12 is not enough
uint32_t ps1 = (8 * memshift * 7 + 64 * memshift * thread);
for (int i = 0; i < 8; i++)
{
uint32_t s1 = ps1 - 8 * memshift * i;
for (int j = 0; j < 3; j++)
(DMatrix + s1)[j] = (state)[j];
round_lyra_v35(state);
}
reduceDuplexV3(state, thread);
reduceDuplexRowSetupV3(1, 0, 2, state, thread);
reduceDuplexRowSetupV3(2, 1, 3, state, thread);
reduceDuplexRowSetupV3(3, 0, 4, state, thread);
reduceDuplexRowSetupV3(4, 3, 5, state, thread);
reduceDuplexRowSetupV3(5, 2, 6, state, thread);
reduceDuplexRowSetupV3(6, 1, 7, state, thread);
uint32_t rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(7, rowa, 0, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(0, rowa, 3, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(3, rowa, 6, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(6, rowa, 1, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(1, rowa, 4, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(4, rowa, 7, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(7, rowa, 2, state, thread);
rowa = ((uint2*)state)[0].x & 7;
reduceDuplexRowtV3(2, rowa, 5, state, thread);
uint32_t shift = (memshift * rowa + 64 * memshift * thread);
for (int j = 0; j < 3; j++)
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
for (int i = 0; i < 12; i++)
round_lyra_v35(state);
outputHash[thread] = ((uint2*)state)[0];
outputHash[thread + threads] = ((uint2*)state)[1];
outputHash[thread + 2 * threads] = ((uint2*)state)[2];
outputHash[thread + 3 * threads] = ((uint2*)state)[3];
} //thread
}
__host__
void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t *hash)
{
cudaMemcpyToSymbol(DMatrix, &hash, sizeof(hash), 0, cudaMemcpyHostToDevice);
}
void lyra2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order)
{
uint32_t tpb;
if (device_sm[device_map[thr_id]]<500)
tpb = 48;
else if (device_sm[device_map[thr_id]]==500)
tpb = 16;
else
tpb = TPB;
dim3 grid((threads + tpb - 1) / tpb);
dim3 block(tpb);
if (device_sm[device_map[thr_id]] == 500)
lyra2_gpu_hash_32 <<< grid, block >>> (threads, startNounce, (uint2*)d_outputHash);
else
lyra2_gpu_hash_32_v3 <<< grid, block >>> (threads, startNounce, (uint2*)d_outputHash);
MyStreamSynchronize(NULL, order, thr_id);
}