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Apply lyra2v2 boost published by NH 

changes only tested/applied to SM 5+ with some cleanup
2upstream
Tanguy Pruvot 9 years ago
parent
8ecf0a9101
commit
53275e3a00
1 changed files with 286 additions and 204 deletions
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  1. 468
      lyra2/cuda_lyra2v2.cu

468
lyra2/cuda_lyra2v2.cu
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@ -2,9 +2,6 @@
#include <stdint.h> #include <stdint.h>
#include <memory.h> #include <memory.h>
#define TPB52 8
#define TPB50 16
#include "cuda_lyra2v2_sm3.cuh" #include "cuda_lyra2v2_sm3.cuh"
#ifdef __INTELLISENSE__ #ifdef __INTELLISENSE__
@ -22,6 +19,23 @@
__device__ uint2x4 *DMatrix; __device__ uint2x4 *DMatrix;
__device__ __forceinline__ uint2 LD4S(const int index)
{
extern __shared__ uint2 shared_mem[];
return shared_mem[(index * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x];
}
__device__ __forceinline__ void ST4S(const int index, const uint2 data)
{
extern __shared__ uint2 shared_mem[];
shared_mem[(index * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data;
}
__device__ __forceinline__ uint2 shuffle2(uint2 a, uint32_t b, uint32_t c)
{
return make_uint2(__shfl(a.x, b, c), __shfl(a.y, b, c));
}
__device__ __forceinline__ __device__ __forceinline__
void Gfunc_v5(uint2 &a, uint2 &b, uint2 &c, uint2 &d) void Gfunc_v5(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
{ {
@ -32,7 +46,7 @@ void Gfunc_v5(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
} }
__device__ __forceinline__ __device__ __forceinline__
void round_lyra_v5(uint2x4* s) void round_lyra_v5(uint2x4 s[4])
{ {
Gfunc_v5(s[0].x, s[1].x, s[2].x, s[3].x); Gfunc_v5(s[0].x, s[1].x, s[2].x, s[3].x);
Gfunc_v5(s[0].y, s[1].y, s[2].y, s[3].y); Gfunc_v5(s[0].y, s[1].y, s[2].y, s[3].y);
@ -46,144 +60,144 @@ void round_lyra_v5(uint2x4* s)
} }
__device__ __forceinline__ __device__ __forceinline__
void reduceDuplex(uint2x4 state[4], const uint32_t thread) void round_lyra_v5(uint2 s[4])
{ {
uint2x4 state1[3]; Gfunc_v5(s[0], s[1], s[2], s[3]);
const uint32_t ps1 = (Nrow * Ncol * memshift * thread); s[1] = shuffle2(s[1], threadIdx.x + 1, 4);
const uint32_t ps2 = (memshift * (Ncol-1) + memshift * Ncol + Nrow * Ncol * memshift * thread); s[2] = shuffle2(s[2], threadIdx.x + 2, 4);
s[3] = shuffle2(s[3], threadIdx.x + 3, 4);
Gfunc_v5(s[0], s[1], s[2], s[3]);
s[1] = shuffle2(s[1], threadIdx.x + 3, 4);
s[2] = shuffle2(s[2], threadIdx.x + 2, 4);
s[3] = shuffle2(s[3], threadIdx.x + 1, 4);
}
#pragma unroll 4 __device__ __forceinline__
void reduceDuplexRowSetup2(uint2 state[4])
{
uint2 state1[Ncol][3], state0[Ncol][3], state2[3];
int i, j;
#pragma unroll
for (int i = 0; i < Ncol; i++) for (int i = 0; i < Ncol; i++)
{ {
uint32_t s1 = ps1 + i*memshift;
uint32_t s2 = ps2 - i*memshift;
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix+s1)[j]); state0[Ncol - i - 1][j] = state[j];
round_lyra_v5(state);
}
//#pragma unroll 4
for (i = 0; i < Ncol; i++)
{
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state[j] ^= state1[j]; state[j] ^= state0[i][j];
round_lyra_v5(state); round_lyra_v5(state);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state1[j] ^= state[j]; state1[Ncol - i - 1][j] = state0[i][j];
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
(DMatrix + s2)[j] = state1[j]; state1[Ncol - i - 1][j] ^= state[j];
} }
}
__device__ __forceinline__
void reduceDuplex50(uint2x4 state[4], const uint32_t thread)
{
const uint32_t ps1 = (Nrow * Ncol * memshift * thread);
const uint32_t ps2 = (memshift * (Ncol - 1) + memshift * Ncol + Nrow * Ncol * memshift * thread);
#pragma unroll 4 for (i = 0; i < Ncol; i++)
for (int i = 0; i < Ncol; i++)
{ {
const uint32_t s1 = ps1 + i*memshift; const uint32_t s0 = memshift * Ncol * 0 + i * memshift;
const int32_t s2 = ps2 - i*memshift; const uint32_t s2 = memshift * Ncol * 2 + memshift * (Ncol - 1) - i*memshift;
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state[j] ^= __ldg4(&(DMatrix + s1)[j]); state[j] ^= state1[i][j] + state0[i][j];
round_lyra_v5(state); round_lyra_v5(state);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
(DMatrix + s2)[j] = __ldg4(&(DMatrix + s1)[j]) ^ state[j]; state2[j] = state1[i][j];
}
}
__device__ __forceinline__ #pragma unroll
void reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, uint2x4 state[4], const uint32_t thread) for (j = 0; j < 3; j++)
{ state2[j] ^= state[j];
uint2x4 state2[3], state1[3];
const uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread);
const uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread);
const uint32_t ps3 = (memshift * (Ncol-1) + memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread);
for (int i = 0; i < Ncol; i++) #pragma unroll
{ for (j = 0; j < 3; j++)
const uint32_t s1 = ps1 + i*memshift; ST4S(s2 + j, state2[j]);
const uint32_t s2 = ps2 + i*memshift;
const uint32_t s3 = ps3 - i*memshift;
#if __CUDA_ARCH__ == 500 uint2 Data0 = shuffle2(state[0], threadIdx.x - 1, 4);
uint2 Data1 = shuffle2(state[1], threadIdx.x - 1, 4);
uint2 Data2 = shuffle2(state[2], threadIdx.x - 1, 4);
#pragma unroll if (threadIdx.x == 0) {
for (int j = 0; j < 3; j++) state0[i][0] ^= Data2;
state[j] = state[j] ^ (__ldg4(&(DMatrix + s1)[j]) + __ldg4(&(DMatrix + s2)[j])); state0[i][1] ^= Data0;
state0[i][2] ^= Data1;
} else {
state0[i][0] ^= Data0;
state0[i][1] ^= Data1;
state0[i][2] ^= Data2;
}
round_lyra_v5(state);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix + s1)[j]); ST4S(s0 + j, state0[i][j]);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state2[j] = __ldg4(&(DMatrix + s2)[j]); state0[i][j] = state2[j];
#pragma unroll
for (int j = 0; j < 3; j++)
{
state1[j] ^= state[j];
(DMatrix + s3)[j] = state1[j];
} }
#else /* 5.2 */ for (i = 0; i < Ncol; i++)
{
const uint32_t s1 = memshift * Ncol * 1 + i*memshift;
const uint32_t s3 = memshift * Ncol * 3 + memshift * (Ncol - 1) - i*memshift;
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix + s1)[j]); state[j] ^= state1[i][j] + state0[Ncol - i - 1][j];
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = __ldg4(&(DMatrix + s2)[j]);
#pragma unroll
for (int j = 0; j < 3; j++)
{
uint2x4 tmp = state1[j] + state2[j];
state[j] ^= tmp;
}
round_lyra_v5(state); round_lyra_v5(state);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
{ state0[Ncol - i - 1][j] ^= state[j];
state1[j] ^= state[j];
(DMatrix + s3)[j] = state1[j];
}
#endif
((uint2*)state2)[0] ^= ((uint2*)state)[11];
#pragma unroll #pragma unroll
for (int j = 0; j < 11; j++) for (j = 0; j < 3; j++)
((uint2*)state2)[j+1] ^= ((uint2*)state)[j]; ST4S(s3 + j, state0[Ncol - i - 1][j]);
uint2 Data0 = shuffle2(state[0], threadIdx.x - 1, 4);
uint2 Data1 = shuffle2(state[1], threadIdx.x - 1, 4);
uint2 Data2 = shuffle2(state[2], threadIdx.x - 1, 4);
if (threadIdx.x == 0) {
state1[i][0] ^= Data2;
state1[i][1] ^= Data0;
state1[i][2] ^= Data1;
} else {
state1[i][0] ^= Data0;
state1[i][1] ^= Data1;
state1[i][2] ^= Data2;
}
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j]; ST4S(s1 + j, state1[i][j]);
} }
} }
__device__
__device__ __forceinline__ void reduceDuplexRowt2(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4])
void reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, uint2x4* state, const uint32_t thread)
{ {
uint2x4 state1[3], state2[3]; uint2 state1[3], state2[3];
const uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread); const uint32_t ps1 = memshift * Ncol * rowIn;
const uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread); const uint32_t ps2 = memshift * Ncol * rowInOut;
const uint32_t ps3 = (memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread); const uint32_t ps3 = memshift * Ncol * rowOut;
for (int i = 0; i < Ncol; i++) for (int i = 0; i < Ncol; i++)
{ {
@ -193,161 +207,220 @@ void reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, u
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix + s1)[j]); state1[j] = LD4S(s1 + j);
#pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = LD4S(s2 + j);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state2[j] = __ldg4(&(DMatrix + s2)[j]); state[j] ^= state1[j] + state2[j];
round_lyra_v5(state);
uint2 Data0 = shuffle2(state[0], threadIdx.x - 1, 4);
uint2 Data1 = shuffle2(state[1], threadIdx.x - 1, 4);
uint2 Data2 = shuffle2(state[2], threadIdx.x - 1, 4);
if (threadIdx.x == 0) {
state2[0] ^= Data2;
state2[1] ^= Data0;
state2[2] ^= Data1;
} else {
state2[0] ^= Data0;
state2[1] ^= Data1;
state2[2] ^= Data2;
}
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j] += state2[j]; ST4S(s2 + j, state2[j]);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state[j] ^= state1[j]; ST4S(s3 + j, LD4S(s3 + j) ^ state[j]);
}
}
round_lyra_v5(state); __device__
void reduceDuplexRowt2x4(const int rowInOut, uint2 state[4])
{
const int rowIn = 2;
const int rowOut = 3;
((uint2*)state2)[0] ^= ((uint2*)state)[11]; int i, j;
uint2 last[3];
const uint32_t ps1 = memshift * Ncol * rowIn;
const uint32_t ps2 = memshift * Ncol * rowInOut;
#pragma unroll #pragma unroll
for (int j = 0; j < 11; j++) for (int j = 0; j < 3; j++)
((uint2*)state2)[j + 1] ^= ((uint2*)state)[j]; last[j] = LD4S(ps2 + j);
#if __CUDA_ARCH__ == 500
if (rowInOut != rowOut)
{
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s3)[j] ^= state[j]; state[j] ^= LD4S(ps1 + j) + last[j];
round_lyra_v5(state);
uint2 Data0 = shuffle2(state[0], threadIdx.x - 1, 4);
uint2 Data1 = shuffle2(state[1], threadIdx.x - 1, 4);
uint2 Data2 = shuffle2(state[2], threadIdx.x - 1, 4);
if (threadIdx.x == 0) {
last[0] ^= Data2;
last[1] ^= Data0;
last[2] ^= Data1;
} else {
last[0] ^= Data0;
last[1] ^= Data1;
last[2] ^= Data2;
} }
if (rowInOut == rowOut) if (rowInOut == rowOut)
{ {
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
state2[j] ^= state[j]; last[j] ^= state[j];
} }
#else
if (rowInOut != rowOut) for (i = 1; i < Ncol; i++)
{ {
const uint32_t s1 = ps1 + i*memshift;
const uint32_t s2 = ps2 + i*memshift;
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (j = 0; j < 3; j++)
(DMatrix + s3)[j] ^= state[j]; state[j] ^= LD4S(s1 + j) + LD4S(s2 + j);
} else {
#pragma unroll round_lyra_v5(state);
for (int j = 0; j < 3; j++)
state2[j] ^= state[j];
} }
#endif
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j]; state[j] ^= last[j];
}
} }
__global__
#if __CUDA_ARCH__ == 500 __launch_bounds__(32, 1)
__global__ __launch_bounds__(TPB50, 1) void lyra2v2_gpu_hash_32_1(uint32_t threads, uint2 *inputHash)
#else
__global__ __launch_bounds__(TPB52, 1)
#endif
void lyra2v2_gpu_hash_32(const uint32_t threads, uint32_t startNounce, uint2 *g_hash)
{ {
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); const uint32_t thread = blockDim.x * blockIdx.x + threadIdx.x;
uint2x4 blake2b_IV[2]; const uint2x4 blake2b_IV[2] = {
0xf3bcc908UL, 0x6a09e667UL, 0x84caa73bUL, 0xbb67ae85UL,
0xfe94f82bUL, 0x3c6ef372UL, 0x5f1d36f1UL, 0xa54ff53aUL,
0xade682d1UL, 0x510e527fUL, 0x2b3e6c1fUL, 0x9b05688cUL,
0xfb41bd6bUL, 0x1f83d9abUL, 0x137e2179UL, 0x5be0cd19UL
};
const uint2x4 Mask[2] = {
0x00000020UL, 0x00000000UL, 0x00000020UL, 0x00000000UL,
0x00000020UL, 0x00000000UL, 0x00000001UL, 0x00000000UL,
0x00000004UL, 0x00000000UL, 0x00000004UL, 0x00000000UL,
0x00000080UL, 0x00000000UL, 0x00000000UL, 0x01000000UL
};
if (threadIdx.x == 0) { uint2x4 state[4];
((uint16*)blake2b_IV)[0] = make_uint16(
0xf3bcc908, 0x6a09e667, 0x84caa73b, 0xbb67ae85,
0xfe94f82b, 0x3c6ef372, 0x5f1d36f1, 0xa54ff53a,
0xade682d1, 0x510e527f, 0x2b3e6c1f, 0x9b05688c,
0xfb41bd6b, 0x1f83d9ab, 0x137e2179, 0x5be0cd19
);
}
if (thread < threads) if (thread < threads)
{ {
uint2x4 state[4]; state[0].x = state[1].x = __ldg(&inputHash[thread + threads * 0]);
state[0].y = state[1].y = __ldg(&inputHash[thread + threads * 1]);
((uint2*)state)[0] = __ldg(&g_hash[thread]); state[0].z = state[1].z = __ldg(&inputHash[thread + threads * 2]);
((uint2*)state)[1] = __ldg(&g_hash[thread + threads]); state[0].w = state[1].w = __ldg(&inputHash[thread + threads * 3]);
((uint2*)state)[2] = __ldg(&g_hash[thread + threads*2]); state[2] = blake2b_IV[0];
((uint2*)state)[3] = __ldg(&g_hash[thread + threads*3]); state[3] = blake2b_IV[1];
state[1] = state[0];
state[2] = ((blake2b_IV)[0]);
state[3] = ((blake2b_IV)[1]);
for (int i = 0; i<12; i++) for (int i = 0; i<12; i++)
round_lyra_v5(state); round_lyra_v5(state);
((uint2*)state)[0].x ^= 0x20; state[0] ^= Mask[0];
((uint2*)state)[1].x ^= 0x20; state[1] ^= Mask[1];
((uint2*)state)[2].x ^= 0x20;
((uint2*)state)[3].x ^= 0x01;
((uint2*)state)[4].x ^= 0x04;
((uint2*)state)[5].x ^= 0x04;
((uint2*)state)[6].x ^= 0x80;
((uint2*)state)[7].y ^= 0x01000000;
for (int i = 0; i<12; i++) for (int i = 0; i<12; i++)
round_lyra_v5(state); round_lyra_v5(state);
const uint32_t ps1 = (memshift * (Ncol - 1) + Nrow * Ncol * memshift * thread); DMatrix[blockDim.x * gridDim.x * 0 + blockDim.x * blockIdx.x + threadIdx.x] = state[0];
DMatrix[blockDim.x * gridDim.x * 1 + blockDim.x * blockIdx.x + threadIdx.x] = state[1];
for (int i = 0; i < Ncol; i++) DMatrix[blockDim.x * gridDim.x * 2 + blockDim.x * blockIdx.x + threadIdx.x] = state[2];
{ DMatrix[blockDim.x * gridDim.x * 3 + blockDim.x * blockIdx.x + threadIdx.x] = state[3];
const uint32_t s1 = ps1 - memshift * i;
DMatrix[s1] = state[0];
DMatrix[s1+1] = state[1];
DMatrix[s1+2] = state[2];
round_lyra_v5(state);
} }
}
reduceDuplex50(state, thread); __global__
__launch_bounds__(32, 1)
void lyra2v2_gpu_hash_32_2(uint32_t threads)
{
const uint32_t thread = blockDim.y * blockIdx.x + threadIdx.y;
reduceDuplexRowSetupV2(1, 0, 2, state, thread); if (thread < threads)
reduceDuplexRowSetupV2(2, 1, 3, state, thread); {
uint2 state[4];
state[0] = ((uint2*)DMatrix)[(0 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x];
state[1] = ((uint2*)DMatrix)[(1 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x];
state[2] = ((uint2*)DMatrix)[(2 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x];
state[3] = ((uint2*)DMatrix)[(3 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x];
reduceDuplexRowSetup2(state);
uint32_t rowa; uint32_t rowa;
int prev=3; int prev = 3;
for (int i = 0; i < 4; i++) for (int i = 0; i < 3; i++)
{ {
rowa = ((uint2*)state)[0].x & 3; rowa = __shfl(state[0].x, 0, 4) & 3;
reduceDuplexRowtV2(prev, rowa, i, state, thread); reduceDuplexRowt2(prev, rowa, i, state);
prev = i; prev = i;
} }
const uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread); rowa = __shfl(state[0].x, 0, 4) & 3;
reduceDuplexRowt2x4(rowa, state);
#pragma unroll ((uint2*)DMatrix)[(0 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x] = state[0];
for (int j = 0; j < 3; j++) ((uint2*)DMatrix)[(1 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x] = state[1];
state[j] ^= __ldg4(&(DMatrix + shift)[j]); ((uint2*)DMatrix)[(2 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x] = state[2];
((uint2*)DMatrix)[(3 * gridDim.x * blockDim.y + thread) * blockDim.x + threadIdx.x] = state[3];
}
}
__global__
__launch_bounds__(32, 1)
void lyra2v2_gpu_hash_32_3(uint32_t threads, uint2 *outputHash)
{
const uint32_t thread = blockDim.x * blockIdx.x + threadIdx.x;
uint2x4 state[4];
if (thread < threads)
{
state[0] = __ldg4(&DMatrix[blockDim.x * gridDim.x * 0 + blockDim.x * blockIdx.x + threadIdx.x]);
state[1] = __ldg4(&DMatrix[blockDim.x * gridDim.x * 1 + blockDim.x * blockIdx.x + threadIdx.x]);
state[2] = __ldg4(&DMatrix[blockDim.x * gridDim.x * 2 + blockDim.x * blockIdx.x + threadIdx.x]);
state[3] = __ldg4(&DMatrix[blockDim.x * gridDim.x * 3 + blockDim.x * blockIdx.x + threadIdx.x]);
for (int i = 0; i < 12; i++) for (int i = 0; i < 12; i++)
round_lyra_v5(state); round_lyra_v5(state);
g_hash[thread] = ((uint2*)state)[0]; outputHash[thread + threads * 0] = state[0].x;
g_hash[thread + threads] = ((uint2*)state)[1]; outputHash[thread + threads * 1] = state[0].y;
g_hash[thread + threads*2] = ((uint2*)state)[2]; outputHash[thread + threads * 2] = state[0].z;
g_hash[thread + threads*3] = ((uint2*)state)[3]; outputHash[thread + threads * 3] = state[0].w;
} }
} }
#else #else
#include "cuda_helper.h" #include "cuda_helper.h"
#if __CUDA_ARCH__ < 200 #if __CUDA_ARCH__ < 200
__device__ void* DMatrix; __device__ void* DMatrix;
#endif #endif
__global__ void lyra2v2_gpu_hash_32(const uint32_t threads, uint32_t startNounce, uint2 *g_hash) {} __global__ void lyra2v2_gpu_hash_32_1(uint32_t threads, uint2 *inputHash) {}
__global__ void lyra2v2_gpu_hash_32_2(uint32_t threads) {}
__global__ void lyra2v2_gpu_hash_32_3(uint32_t threads, uint2 *outputHash) {}
#endif #endif
__host__ __host__
void lyra2v2_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix) void lyra2v2_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix)
{ {
@ -360,21 +433,30 @@ __host__
void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *g_hash, int order) void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *g_hash, int order)
{ {
int dev_id = device_map[thr_id % MAX_GPUS]; int dev_id = device_map[thr_id % MAX_GPUS];
uint32_t tpb = TPB52;
if (cuda_arch[dev_id] > 500) tpb = TPB52; if (device_sm[dev_id] >= 500) {
else if (cuda_arch[dev_id] == 500) tpb = TPB50;
else if (cuda_arch[dev_id] >= 350) tpb = TPB35; const uint32_t tpb = 32;
dim3 grid2((threads + tpb - 1) / tpb);
dim3 block2(tpb);
dim3 grid4((threads * 4 + tpb - 1) / tpb);
dim3 block4(4, tpb / 4);
lyra2v2_gpu_hash_32_1 <<< grid2, block2 >>> (threads, (uint2*)g_hash);
lyra2v2_gpu_hash_32_2 <<< grid4, block4, 48 * sizeof(uint2) * tpb >>> (threads);
lyra2v2_gpu_hash_32_3 <<< grid2, block2 >>> (threads, (uint2*)g_hash);
} else {
uint32_t tpb = 16;
if (cuda_arch[dev_id] >= 350) tpb = TPB35;
else if (cuda_arch[dev_id] >= 300) tpb = TPB30; else if (cuda_arch[dev_id] >= 300) tpb = TPB30;
else if (cuda_arch[dev_id] >= 200) tpb = TPB20; else if (cuda_arch[dev_id] >= 200) tpb = TPB20;
dim3 grid((threads + tpb - 1) / tpb); dim3 grid((threads + tpb - 1) / tpb);
dim3 block(tpb); dim3 block(tpb);
lyra2v2_gpu_hash_32_v3 <<< grid, block >>> (threads, startNounce, (uint2*)g_hash);
if (device_sm[dev_id] >= 500 && cuda_arch[dev_id] >= 500) }
lyra2v2_gpu_hash_32 <<<grid, block>>> (threads, startNounce, (uint2*)g_hash);
else
lyra2v2_gpu_hash_32_v3 <<<grid, block>>> (threads, startNounce, (uint2*)g_hash);
//MyStreamSynchronize(NULL, order, thr_id);
} }

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