GOSTSec/ccminer-gostd-lite
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

lyra2: improve cuda implementation (part 1, SM5+)

Browse Source 
based on the new djm34 method, 2x faster than first version

cleaned and tuned for the GTX 750/960 (linux / cuda 6.5)
This commit is contained in:
Tanguy Pruvot 2015-10-13 00:25:14 +02:00
parent 9dfa757dc7
commit fc84c719e9
7 changed files with 240 additions and 203 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
Algo256/cuda_groestl256.cu
View File

@ -176,7 +176,7 @@ void groestl256_perm_Q(uint32_t thread, uint32_t *a, char *mixtabs)
}
__global__ __launch_bounds__(256,1)
void groestl256_gpu_hash32(uint32_t threads, uint32_t startNounce, uint64_t *outputHash, uint32_t *resNonces)
void groestl256_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint64_t *outputHash, uint32_t *resNonces)
{
#if USE_SHARED
extern __shared__ char mixtabs[];
@ -315,7 +315,7 @@ uint32_t groestl256_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNoun
#else
size_t shared_size = 0;
#endif
groestl256_gpu_hash32<<<grid, block, shared_size>>>(threads, startNounce, d_outputHash, d_GNonces[thr_id]);
groestl256_gpu_hash_32<<<grid, block, shared_size>>>(threads, startNounce, d_outputHash, d_GNonces[thr_id]);
MyStreamSynchronize(NULL, order, thr_id);

3
README.txt
View File

@ -229,9 +229,10 @@ features.
>>> RELEASE HISTORY <<<
Under Dev... v1.7
Improve lyra2 (v1) cuda implementation
Restore whirlpool algo (and whirlcoin variant)
Prepare algo switch ability
Add --benchmark -a all to run a benchmark for all algos
Add --benchmark alone to run a benchmark for all algos
Add --cuda-schedule parameter
Add --show-diff parameter, which display shares diff,
and is able to detect real solved blocks on pools.

1
bench.cpp
View File

@ -98,7 +98,6 @@ bool bench_algo_switch_next(int thr_id)
if (algo == ALGO_DMD_GR) algo++; // same as groestl
if (algo == ALGO_WHIRLCOIN) algo++; // same as whirlpool
// and unwanted ones...
if (algo == ALGO_LYRA2) algo++; // weird memory leak to fix (uint2 Matrix[96][8] too big)
if (algo == ALGO_SCRYPT) algo++;
if (algo == ALGO_SCRYPT_JANE) algo++;

1
configure.sh
View File

@ -1,4 +1,5 @@
# possible additional CUDA_CFLAGS
#-gencode=arch=compute_52,code=\"sm_52,compute_52\"
#-gencode=arch=compute_50,code=\"sm_50,compute_50\"
#-gencode=arch=compute_35,code=\"sm_35,compute_35\"
#-gencode=arch=compute_30,code=\"sm_30,compute_30\"

383
lyra2/cuda_lyra2.cu
View File

@ -1,71 +1,26 @@
/**
* Lyra2 (v1) cuda implementation based on djm34 work - SM 5/5.2
* tpruvot@github 2015
*/
#include <stdio.h>
#include <memory.h>
#include "cuda_helper.h"
#include "cuda_lyra2_vectors.h"
#define TPB 160
#define TPB50 16
#define TPB52 8
static __constant__ uint2 blake2b_IV[8] = {
{ 0xf3bcc908, 0x6a09e667 },
{ 0x84caa73b, 0xbb67ae85 },
{ 0xfe94f82b, 0x3c6ef372 },
{ 0x5f1d36f1, 0xa54ff53a },
{ 0xade682d1, 0x510e527f },
{ 0x2b3e6c1f, 0x9b05688c },
{ 0xfb41bd6b, 0x1f83d9ab },
{ 0x137e2179, 0x5be0cd19 }
};
#define uint2x4 uint28
#define memshift 3
#define reduceDuplexRow(rowIn, rowInOut, rowOut) { \
for (int i = 0; i < 8; i++) { \
for (int j = 0; j < 12; j++) \
state[j] ^= Matrix[12 * i + j][rowIn] + Matrix[12 * i + j][rowInOut]; \
round_lyra(state); \
for (int j = 0; j < 12; j++) \
Matrix[j + 12 * i][rowOut] ^= state[j]; \
Matrix[0 + 12 * i][rowInOut] ^= state[11]; \
Matrix[1 + 12 * i][rowInOut] ^= state[0]; \
Matrix[2 + 12 * i][rowInOut] ^= state[1]; \
Matrix[3 + 12 * i][rowInOut] ^= state[2]; \
Matrix[4 + 12 * i][rowInOut] ^= state[3]; \
Matrix[5 + 12 * i][rowInOut] ^= state[4]; \
Matrix[6 + 12 * i][rowInOut] ^= state[5]; \
Matrix[7 + 12 * i][rowInOut] ^= state[6]; \
Matrix[8 + 12 * i][rowInOut] ^= state[7]; \
Matrix[9 + 12 * i][rowInOut] ^= state[8]; \
Matrix[10+ 12 * i][rowInOut] ^= state[9]; \
Matrix[11+ 12 * i][rowInOut] ^= state[10]; \
} \
}
#define Ncol 8
#define NcolMask 0x7
#define absorbblock(in) { \
state[0] ^= Matrix[0][in]; \
state[1] ^= Matrix[1][in]; \
state[2] ^= Matrix[2][in]; \
state[3] ^= Matrix[3][in]; \
state[4] ^= Matrix[4][in]; \
state[5] ^= Matrix[5][in]; \
state[6] ^= Matrix[6][in]; \
state[7] ^= Matrix[7][in]; \
state[8] ^= Matrix[8][in]; \
state[9] ^= Matrix[9][in]; \
state[10] ^= Matrix[10][in]; \
state[11] ^= Matrix[11][in]; \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
round_lyra(state); \
}
__device__ uint2x4* DMatrix;
static __device__ __forceinline__
void Gfunc(uint2 & a, uint2 &b, uint2 &c, uint2 &d)
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);
@ -73,151 +28,233 @@ void Gfunc(uint2 & a, uint2 &b, uint2 &c, uint2 &d)
c += d; b ^= c; b = ROR2(b, 63);
}
__device__ __forceinline__
static void round_lyra(uint2 *s)
static __device__ __forceinline__
void round_lyra(uint2x4* s)
{
Gfunc(s[0], s[4], s[8], s[12]);
Gfunc(s[1], s[5], s[9], s[13]);
Gfunc(s[2], s[6], s[10], s[14]);
Gfunc(s[3], s[7], s[11], s[15]);
Gfunc(s[0], s[5], s[10], s[15]);
Gfunc(s[1], s[6], s[11], s[12]);
Gfunc(s[2], s[7], s[8], s[13]);
Gfunc(s[3], s[4], s[9], s[14]);
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);
}
__device__ __forceinline__
void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2 state[16], uint2 Matrix[96][8])
static __device__ __forceinline__
void reduceDuplex(uint2x4 state[4], uint32_t thread)
{
#if __CUDA_ARCH__ > 500
#pragma unroll
#endif
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++)
{
#pragma unroll
for (int j = 0; j < 12; j++)
state[j] ^= Matrix[12 * i + j][rowIn] + Matrix[12 * i + j][rowInOut];
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);
#pragma unroll
for (int j = 0; j < 12; j++)
Matrix[j + 84 - 12 * i][rowOut] = Matrix[12 * i + j][rowIn] ^ state[j];
Matrix[0 + 12 * i][rowInOut] ^= state[11];
Matrix[1 + 12 * i][rowInOut] ^= state[0];
Matrix[2 + 12 * i][rowInOut] ^= state[1];
Matrix[3 + 12 * i][rowInOut] ^= state[2];
Matrix[4 + 12 * i][rowInOut] ^= state[3];
Matrix[5 + 12 * i][rowInOut] ^= state[4];
Matrix[6 + 12 * i][rowInOut] ^= state[5];
Matrix[7 + 12 * i][rowInOut] ^= state[6];
Matrix[8 + 12 * i][rowInOut] ^= state[7];
Matrix[9 + 12 * i][rowInOut] ^= state[8];
Matrix[10 + 12 * i][rowInOut] ^= state[9];
Matrix[11 + 12 * i][rowInOut] ^= state[10];
for (int j = 0; j < 3; j++)
state1[j] ^= state[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state1[j];
}
}
__global__ __launch_bounds__(TPB, 1)
void lyra2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint64_t *outputHash)
static __device__ __forceinline__
void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2x4 state[4], uint32_t thread)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
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)
{
uint2 state[16];
uint2x4 state[4];
#pragma unroll
for (int i = 0; i<4; i++) {
LOHI(state[i].x, state[i].y, outputHash[threads*i + thread]);
} //password
((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]);
#pragma unroll
for (int i = 0; i<4; i++) {
state[i + 4] = state[i];
} //salt
state[1] = state[0];
state[2] = blake2b_IV[0];
state[3] = blake2b_IV[1];
#pragma unroll
for (int i = 0; i<8; i++) {
state[i + 8] = blake2b_IV[i];
}
for (int i = 0; i<24; i++)
round_lyra(state); //because 12 is not enough
// blake2blyra x2
//#pragma unroll 24
for (int i = 0; i<24; i++) {
round_lyra(state);
} //because 12 is not enough
uint2 Matrix[96][8]; // not cool
// reducedSqueezeRow0
#pragma unroll 8
const uint32_t ps1 = (memshift * 7 + 256 * thread);
for (int i = 0; i < 8; i++)
{
#pragma unroll 12
for (int j = 0; j<12; j++) {
Matrix[j + 84 - 12 * i][0] = state[j];
}
const uint32_t s1 = ps1 - memshift * i;
for (int j = 0; j < 3; j++)
(DMatrix + s1)[j] = (state)[j];
round_lyra(state);
}
// reducedSqueezeRow1
#pragma unroll 8
for (int i = 0; i < 8; i++)
{
#pragma unroll 12
for (int j = 0; j<12; j++) {
state[j] ^= Matrix[j + 12 * i][0];
}
round_lyra(state);
#pragma unroll 12
for (int j = 0; j<12; j++) {
Matrix[j + 84 - 12 * i][1] = Matrix[j + 12 * i][0] ^ state[j];
}
}
reduceDuplex(state, thread);
reduceDuplexRowSetup(1, 0, 2,state, Matrix);
reduceDuplexRowSetup(2, 1, 3, state, Matrix);
reduceDuplexRowSetup(3, 0, 4, state, Matrix);
reduceDuplexRowSetup(4, 3, 5, state, Matrix);
reduceDuplexRowSetup(5, 2, 6, state, Matrix);
reduceDuplexRowSetup(6, 1, 7, state, Matrix);
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;
rowa = state[0].x & 7;
reduceDuplexRow(7, rowa, 0);
rowa = state[0].x & 7;
reduceDuplexRow(0, rowa, 3);
rowa = state[0].x & 7;
reduceDuplexRow(3, rowa, 6);
rowa = state[0].x & 7;
reduceDuplexRow(6, rowa, 1);
rowa = state[0].x & 7;
reduceDuplexRow(1, rowa, 4);
rowa = state[0].x & 7;
reduceDuplexRow(4, rowa, 7);
rowa = state[0].x & 7;
reduceDuplexRow(7, rowa, 2);
rowa = state[0].x & 7;
reduceDuplexRow(2, rowa, 5);
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);
absorbblock(rowa);
const int32_t shift = (memshift * 8 * rowa + 256 * thread);
#pragma unroll
for (int i = 0; i<4; i++) {
outputHash[threads*i + thread] = devectorize(state[i]);
} //password
for (int j = 0; j < 3; j++)
state[j] ^= __ldg4(&(DMatrix + shift)[j]);
} //thread
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];
}
}
__host__
void lyra2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order)
void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t* d_matrix)
{
const uint32_t threadsperblock = TPB;
dim3 grid((threads + threadsperblock - 1) / threadsperblock);
dim3 block(threadsperblock);
lyra2_gpu_hash_32 <<<grid, block>>> (threads, startNounce, d_outputHash);
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;
dim3 grid((threads + tpb - 1) / tpb);
dim3 block(tpb);
lyra2_gpu_hash_32 <<< grid, block >>> (threads, startNounce, (uint2*)d_hash);
}

22
lyra2/lyra2RE.cu
View File

@ -10,7 +10,7 @@ extern "C" {
#include "cuda_helper.h"
static uint64_t* d_hash[MAX_GPUS];
//static uint64_t* d_matrix[MAX_GPUS];
static uint64_t* d_matrix[MAX_GPUS];
extern void blake256_cpu_init(int thr_id, uint32_t threads);
extern void blake256_cpu_hash_80(const int thr_id, const uint32_t threads, const uint32_t startNonce, uint64_t *Hash, int order);
@ -21,7 +21,7 @@ extern void keccak256_cpu_free(int thr_id);
extern void skein256_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNonce, uint64_t *d_outputHash, int order);
extern void skein256_cpu_init(int thr_id, uint32_t threads);
//extern void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t *hash);
extern void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix);
extern void lyra2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNonce, uint64_t *d_outputHash, int order);
extern void groestl256_cpu_init(int thr_id, uint32_t threads);
@ -84,17 +84,17 @@ extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce,
uint32_t *pdata = work->data;
uint32_t *ptarget = work->target;
const uint32_t first_nonce = pdata[19];
int intensity = (device_sm[device_map[thr_id]] >= 500 && !is_windows()) ? 18 : 17;
int intensity = (device_sm[device_map[thr_id]] >= 500 && !is_windows()) ? 17 : 16;
uint32_t throughput = cuda_default_throughput(thr_id, 1U << intensity); // 18=256*256*4;
if (init[thr_id]) throughput = min(throughput, max_nonce - first_nonce);
if (opt_benchmark)
ptarget[7] = 0x00ff;
ptarget[7] = 0x000f;
if (!init[thr_id])
{
cudaSetDevice(device_map[thr_id]);
cudaGetLastError(); // reset last error
CUDA_LOG_ERROR();
blake256_cpu_init(thr_id, throughput);
keccak256_cpu_init(thr_id,throughput);
@ -102,8 +102,8 @@ extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce,
groestl256_cpu_init(thr_id, throughput);
// DMatrix
// cudaMalloc(&d_matrix[thr_id], (size_t)16 * 8 * 8 * sizeof(uint64_t) * throughput);
// lyra2_cpu_init(thr_id, throughput, d_matrix[thr_id]);
cudaMalloc(&d_matrix[thr_id], (size_t)16 * 8 * 8 * sizeof(uint64_t) * throughput);
lyra2_cpu_init(thr_id, throughput, d_matrix[thr_id]);
CUDA_SAFE_CALL(cudaMalloc(&d_hash[thr_id], (size_t)32 * throughput));
@ -147,7 +147,7 @@ extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce,
lyra2re_hash(vhash64, endiandata);
if (vhash64[7] <= ptarget[7] && fulltest(vhash64, ptarget)) {
if (opt_debug)
applog(LOG_BLUE, "GPU #%d: found second nonce %08x", device_map[thr_id], secNonce);
gpulog(LOG_BLUE, thr_id, "found second nonce %08x", secNonce);
if (bn_hash_target_ratio(vhash64, ptarget) > work->shareratio)
work_set_target_ratio(work, vhash64);
pdata[21] = secNonce;
@ -157,7 +157,7 @@ extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce,
pdata[19] = foundNonce;
return res;
} else {
applog(LOG_WARNING, "GPU #%d: result for %08x does not validate on CPU!", device_map[thr_id], foundNonce);
gpulog(LOG_WARNING, thr_id, "result for %08x does not validate on CPU!", foundNonce);
}
}
@ -174,10 +174,10 @@ extern "C" void free_lyra2(int thr_id)
if (!init[thr_id])
return;
cudaSetDevice(device_map[thr_id]);
cudaThreadSynchronize();
cudaFree(d_hash[thr_id]);
//cudaFree(d_matrix[thr_id]);
cudaFree(d_matrix[thr_id]);
keccak256_cpu_free(thr_id);
groestl256_cpu_free(thr_id);

29
lyra2/lyra2REv2.cu
View File

@ -92,27 +92,27 @@ extern "C" int scanhash_lyra2v2(int thr_id, struct work* work, uint32_t max_nonc
{
cudaSetDevice(dev_id);
//cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync);
//if (opt_n_gputhreads == 1)
//if (gpu_threads == 1)
// cudaDeviceSetCacheConfig(cudaFuncCachePreferL1);
cudaGetLastError();
CUDA_LOG_ERROR();
blake256_cpu_init(thr_id, throughput);
keccak256_cpu_init(thr_id,throughput);
skein256_cpu_init(thr_id, throughput);
bmw256_cpu_init(thr_id, throughput);
if (device_sm[device_map[thr_id]] < 300) {
applog(LOG_ERR, "Device SM 3.0 or more recent required!");
proper_exit(1);
return -1;
}
// DMatrix (780Ti may prefer 16 instead of 12, cf djm34)
CUDA_SAFE_CALL(cudaMalloc(&d_matrix[thr_id], (size_t)12 * sizeof(uint64_t) * 4 * 4 * throughput));
lyra2v2_cpu_init(thr_id, throughput, d_matrix[thr_id]);
CUDA_SAFE_CALL(cudaMalloc(&d_hash[thr_id], (size_t)32 * throughput));
if (device_sm[dev_id] < 300) {
applog(LOG_ERR, "Device SM 3.0 or more recent required!");
proper_exit(1);
return -1;
}
init[thr_id] = true;
}
@ -153,18 +153,18 @@ extern "C" int scanhash_lyra2v2(int thr_id, struct work* work, uint32_t max_nonc
{
be32enc(&endiandata[19], foundNonces[1]);
lyra2v2_hash(vhash64, endiandata);
if (bn_hash_target_ratio(vhash64, ptarget) > work->shareratio)
work_set_target_ratio(work, vhash64);
pdata[21] = foundNonces[1];
//xchg(pdata[19], pdata[21]);
if (bn_hash_target_ratio(vhash64, ptarget) > work->shareratio) {
work_set_target_ratio(work, vhash64);
xchg(pdata[19], pdata[21]);
}
res++;
}
MyStreamSynchronize(NULL, 0, device_map[thr_id]);
return res;
}
else
{
applog(LOG_WARNING, "GPU #%d: result does not validate on CPU!", dev_id);
gpulog(LOG_WARNING, thr_id, "result for %08x does not validate on CPU!", foundNonces[0]);
}
}
@ -173,7 +173,6 @@ extern "C" int scanhash_lyra2v2(int thr_id, struct work* work, uint32_t max_nonc
} while (!work_restart[thr_id].restart && (max_nonce > ((uint64_t)(pdata[19]) + throughput)));
*hashes_done = pdata[19] - first_nonce + 1;
MyStreamSynchronize(NULL, 0, device_map[thr_id]);
return 0;
}
@ -183,7 +182,7 @@ extern "C" void free_lyra2v2(int thr_id)
if (!init[thr_id])
return;
cudaSetDevice(device_map[thr_id]);
cudaThreadSynchronize();
cudaFree(d_hash[thr_id]);
cudaFree(d_matrix[thr_id]);