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lyra2v2: improve speed on SM 5.2 (Cuda 6.5) with sp unrolls 

Reduce a bit the 750Ti speed but improve a lot the 9xx speed.

Keep compat for SM 3/3.5 in a second file..

Note: With this code and Cuda 7.5, the speed won is the reverse...
      May be "reverted" soon
2upstream
Tanguy Pruvot 9 years ago
parent
2ebcd1fbd5
commit
b3adebdf2a
4 changed files with 397 additions and 291 deletions
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  1. 1
      ccminer.vcxproj
  2. 5
      ccminer.vcxproj.filters
  3. 429
      lyra2/cuda_lyra2v2.cu
  4. 253
      lyra2/cuda_lyra2v2_sm3.cuh

1
ccminer.vcxproj
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@ -334,6 +334,7 @@
<ClInclude Include="uint256.h" /> <ClInclude Include="uint256.h" />
<ClInclude Include="lyra2\Lyra2.h" /> <ClInclude Include="lyra2\Lyra2.h" />
<ClInclude Include="lyra2\Sponge.h" /> <ClInclude Include="lyra2\Sponge.h" />
<ClInclude Include="lyra2\cuda_lyra2v2_sm3.cuh" />
<ClInclude Include="quark\groestl_transf_quad.h" /> <ClInclude Include="quark\groestl_transf_quad.h" />
<ClInclude Include="quark\groestl_functions_quad.h" /> <ClInclude Include="quark\groestl_functions_quad.h" />
</ItemGroup> </ItemGroup>

5
ccminer.vcxproj.filters
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@ -610,6 +610,9 @@
<CudaCompile Include="lyra2\lyra2REv2.cu"> <CudaCompile Include="lyra2\lyra2REv2.cu">
<Filter>Source Files\CUDA</Filter> <Filter>Source Files\CUDA</Filter>
</CudaCompile> </CudaCompile>
<ClInclude Include="lyra2\cuda_lyra2v2_sm3.cuh">
<Filter>Source Files\CUDA</Filter>
</ClInclude>
<CudaCompile Include="zr5.cu"> <CudaCompile Include="zr5.cu">
<Filter>Source Files\CUDA</Filter> <Filter>Source Files\CUDA</Filter>
</CudaCompile> </CudaCompile>
@ -674,4 +677,4 @@
<Filter>Ressources</Filter> <Filter>Ressources</Filter>
</Text> </Text>
</ItemGroup> </ItemGroup>
</Project> </Project>

429
lyra2/cuda_lyra2v2.cu
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@ -6,66 +6,51 @@
#define __CUDA_ARCH__ 500 #define __CUDA_ARCH__ 500
#endif #endif
#define TPB52 10
#define TPB50 16
#include "cuda_lyra2_vectors.h" #include "cuda_lyra2_vectors.h"
#define TPB 16 #include "cuda_lyra2v2_sm3.cuh"
#ifndef __CUDA_ARCH__
__device__ void *DMatrix;
#endif
#if __CUDA_ARCH__ >= 500
#define Nrow 4 #define Nrow 4
#define Ncol 4 #define Ncol 4
#if __CUDA_ARCH__ < 500
#define vectype ulonglong4
#define u64type uint64_t
#define memshift 4
#elif __CUDA_ARCH__ == 500
#define u64type uint2 #define u64type uint2
#define vectype uint28 #define vectype uint28
#define memshift 3 #define memshift 3
#else
#define u64type uint2
#define vectype uint28
#define memshift 3
#endif
__device__ vectype *DMatrix; __device__ vectype *DMatrix;
#if __CUDA_ARCH__ >= 300 __device__ __forceinline__
void Gfunc_v5(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
#if __CUDA_ARCH__ >= 500
static __device__ __forceinline__
void Gfunc_v35(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
{ {
a += b; d ^= a; d = SWAPUINT2(d); a += b; d ^= a; d = SWAPUINT2(d);
c += d; b ^= c; b = ROR24(b); c += d; b ^= c; b = ROR24(b);
a += b; d ^= a; d = ROR16(d); a += b; d ^= a; d = ROR16(d);
c += d; b ^= c; b = ROR2(b, 63); c += d; b ^= c; b = ROR2(b, 63);
} }
#else
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__ __device__ __forceinline__
void round_lyra_v35(vectype* s) void round_lyra_v5(vectype* s)
{ {
Gfunc_v35(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_v35(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);
Gfunc_v35(s[0].z, s[1].z, s[2].z, s[3].z); Gfunc_v5(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_v5(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_v5(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_v5(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_v5(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); Gfunc_v5(s[0].w, s[1].x, s[2].y, s[3].z);
} }
static __device__ __forceinline__ __device__ __forceinline__
void reduceDuplex(vectype state[4], uint32_t thread) void reduceDuplex(vectype state[4], uint32_t thread)
{ {
vectype state1[3]; vectype state1[3];
@ -82,360 +67,217 @@ void reduceDuplex(vectype state[4], uint32_t thread)
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix+s1)[j]); state1[j] = __ldg4(&(DMatrix+s1)[j]);
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state[j] ^= state1[j]; state[j] ^= state1[j];
round_lyra_v35(state); round_lyra_v5(state);
#pragma unroll #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j] ^= state[j]; state1[j] ^= state[j];
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state1[j]; (DMatrix + s2)[j] = state1[j];
} }
} }
static __device__ __forceinline__ __device__ __forceinline__
void reduceDuplexV3(vectype state[4], uint32_t thread) void reduceDuplex50(vectype state[4], uint32_t thread)
{ {
vectype state1[3];
uint32_t ps1 = (Nrow * Ncol * memshift * thread); uint32_t ps1 = (Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * (Ncol - 1) * Nrow + memshift * 1 + Nrow * Ncol * memshift * thread); uint32_t ps2 = (memshift * (Ncol - 1) + memshift * Ncol + Nrow * Ncol * memshift * thread);
#pragma unroll 4 #pragma unroll 4
for (int i = 0; i < Ncol; i++) for (int i = 0; i < Ncol; i++)
{ {
uint32_t s1 = ps1 + Nrow * i *memshift; uint32_t s1 = ps1 + i*memshift;
uint32_t s2 = ps2 - Nrow * i *memshift; uint32_t s2 = ps2 - i*memshift;
for (int j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix + s1)[j]);
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state[j] ^= state1[j]; state[j] ^= __ldg4(&(DMatrix + s1)[j]);
round_lyra_v35(state);
for (int j = 0; j < 3; j++) round_lyra_v5(state);
state1[j] ^= state[j];
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state1[j]; (DMatrix + s2)[j] = __ldg4(&(DMatrix + s1)[j]) ^ state[j];
} }
} }
static __device__ __forceinline__ __device__ __forceinline__
void reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread) void reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
{ {
vectype state2[3],state1[3]; vectype state2[3], state1[3];
uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread); uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread); uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread);
uint32_t ps3 = (memshift * (Ncol-1) + memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread); uint32_t ps3 = (memshift * (Ncol-1) + memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread);
//#pragma unroll 1
for (int i = 0; i < Ncol; i++) for (int i = 0; i < Ncol; i++)
{ {
uint32_t s1 = ps1 + i*memshift; uint32_t s1 = ps1 + i*memshift;
uint32_t s2 = ps2 + i*memshift; uint32_t s2 = ps2 + i*memshift;
uint32_t s3 = ps3 - i*memshift; uint32_t s3 = ps3 - i*memshift;
#if __CUDA_ARCH__ == 500
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j]= __ldg4(&(DMatrix + s1)[j]); state[j] = state[j] ^ (__ldg4(&(DMatrix + s1)[j]) + __ldg4(&(DMatrix + s2)[j]));
round_lyra_v5(state);
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state2[j]= __ldg4(&(DMatrix + s2)[j]); state1[j] = __ldg4(&(DMatrix + s1)[j]);
for (int j = 0; j < 3; j++) {
vectype tmp = state1[j] + state2[j];
state[j] ^= tmp;
}
round_lyra_v35(state); #pragma unroll
for (int j = 0; j < 3; j++)
state2[j] = __ldg4(&(DMatrix + s2)[j]);
for (int j = 0; j < 3; j++) { #pragma unroll
for (int j = 0; j < 3; j++)
{
state1[j] ^= state[j]; state1[j] ^= state[j];
(DMatrix + s3)[j] = state1[j]; (DMatrix + s3)[j] = state1[j];
} }
((uint2*)state2)[0] ^= ((uint2*)state)[11]; #else /* 5.2 */
for (int j = 0; j < 11; j++)
((uint2*)state2)[j+1] ^= ((uint2*)state)[j];
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j]; state1[j] = __ldg4(&(DMatrix + s1)[j]);
} #pragma unroll
}
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 + Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
uint32_t ps3 = (Nrow * memshift * (Ncol - 1) + memshift * rowOut + Nrow * Ncol * memshift * thread);
for (int i = 0; i < Ncol; i++)
{
uint32_t s1 = ps1 + Nrow*i*memshift;
uint32_t s2 = ps2 + Nrow*i*memshift;
uint32_t s3 = ps3 - Nrow*i*memshift;
for (int j = 0; j < 3; j++) 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++) 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]; vectype tmp = state1[j] + state2[j];
state[j] ^= tmp; state[j] ^= tmp;
} }
round_lyra_v35(state); round_lyra_v5(state);
for (int j = 0; j < 3; j++) { #pragma unroll
for (int j = 0; j < 3; j++)
{
state1[j] ^= state[j]; state1[j] ^= state[j];
(DMatrix + s3)[j] = state1[j]; (DMatrix + s3)[j] = state1[j];
} }
#endif
((uint2*)state2)[0] ^= ((uint2*)state)[11]; ((uint2*)state2)[0] ^= ((uint2*)state)[11];
#pragma unroll
for (int j = 0; j < 11; j++) for (int j = 0; j < 11; j++)
((uint2*)state2)[j + 1] ^= ((uint2*)state)[j]; ((uint2*)state2)[j+1] ^= ((uint2*)state)[j];
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j]; (DMatrix + s2)[j] = state2[j];
} }
} }
static __device__ __forceinline__ __device__ __forceinline__
void reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread) void reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
{ {
vectype state1[3],state2[3]; vectype state1[3],state2[3];
uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread); uint32_t ps1 = (memshift * Ncol * rowIn + Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread); uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread);
uint32_t ps3 = (memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread); uint32_t ps3 = (memshift * Ncol * rowOut + Nrow * Ncol * memshift * thread);
//#pragma unroll 1
for (int i = 0; i < Ncol; i++) for (int i = 0; i < Ncol; i++)
{ {
uint32_t s1 = ps1 + i*memshift; uint32_t s1 = ps1 + i*memshift;
uint32_t s2 = ps2 + i*memshift; uint32_t s2 = ps2 + i*memshift;
uint32_t s3 = ps3 + i*memshift; uint32_t s3 = ps3 + i*memshift;
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j] = __ldg4(&(DMatrix + s1)[j]); state1[j] = __ldg4(&(DMatrix + s1)[j]);
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state2[j] = __ldg4(&(DMatrix + s2)[j]); state2[j] = __ldg4(&(DMatrix + s2)[j]);
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state1[j] += state2[j]; state1[j] += state2[j];
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state[j] ^= state1[j]; state[j] ^= state1[j];
round_lyra_v35(state); round_lyra_v5(state);
((uint2*)state2)[0] ^= ((uint2*)state)[11]; ((uint2*)state2)[0] ^= ((uint2*)state)[11];
#pragma unroll
for (int j = 0; j < 11; j++) for (int j = 0; j < 11; j++)
((uint2*)state2)[j + 1] ^= ((uint2*)state)[j]; ((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
if (rowInOut != rowOut) { #if __CUDA_ARCH__ == 500
if (rowInOut != rowOut)
for (int j = 0; j < 3; j++) {
(DMatrix + s2)[j] = state2[j]; #pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
(DMatrix + s3)[j] ^= state[j]; (DMatrix + s3)[j] ^= state[j];
} else { }
if (rowInOut == rowOut)
{
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state2[j] ^= state[j]; state2[j] ^= state[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j]=state2[j];
} }
#else
} if (rowInOut != rowOut)
} {
#pragma unroll
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 + Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
uint32_t ps3 = (memshift * rowOut + Nrow * Ncol * memshift * thread);
#pragma nounroll
for (int i = 0; i < Ncol; i++)
{
uint32_t s1 = ps1 + Nrow * i*memshift;
uint32_t s2 = ps2 + Nrow * i*memshift;
uint32_t s3 = ps3 + Nrow * 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++) for (int j = 0; j < 3; j++)
(DMatrix + s3)[j] ^= state[j]; (DMatrix + s3)[j] ^= state[j];
} else { } else {
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state2[j] ^= state[j]; state2[j] ^= state[j];
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j];
} }
#endif
#pragma unroll
for (int j = 0; j < 3; j++)
(DMatrix + s2)[j] = state2[j];
} }
} }
#if __CUDA_ARCH__ < 500 #if __CUDA_ARCH__ == 500
__global__ __launch_bounds__(128, 1) __global__ __launch_bounds__(TPB50, 1)
#elif __CUDA_ARCH__ == 500
__global__ __launch_bounds__(16, 1)
#else #else
__global__ __launch_bounds__(TPB, 1) __global__ __launch_bounds__(TPB52, 1)
#endif #endif
void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
{ {
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x); const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
vectype state[4]; vectype state[4];
uint28 blake2b_IV[2];
uint28 padding[2];
if (threadIdx.x == 0) {
((uint16*)blake2b_IV)[0] = make_uint16(
0xf3bcc908, 0x6a09e667 , 0x84caa73b, 0xbb67ae85 ,
0xfe94f82b, 0x3c6ef372 , 0x5f1d36f1, 0xa54ff53a ,
0xade682d1, 0x510e527f , 0x2b3e6c1f, 0x9b05688c ,
0xfb41bd6b, 0x1f83d9ab , 0x137e2179, 0x5be0cd19
);
((uint16*)padding)[0] = make_uint16(
0x20, 0x0 , 0x20, 0x0 , 0x20, 0x0 , 0x01, 0x0 ,
0x04, 0x0 , 0x04, 0x0 , 0x80, 0x0 , 0x0, 0x01000000
);
}
#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] = shuffle4(((vectype*)blake2b_IV)[0], 0);
state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0);
for (int i = 0; i<12; i++)
round_lyra_v35(state);
state[0] ^= shuffle4(((vectype*)padding)[0], 0);
state[1] ^= shuffle4(((vectype*)padding)[1], 0);
for (int i = 0; i<12; i++)
round_lyra_v35(state);
uint32_t ps1 = (4 * memshift * 3 + 16 * memshift * thread);
//#pragma unroll 4
for (int i = 0; i < 4; i++)
{
uint32_t s1 = ps1 - 4 * 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);
uint32_t rowa;
int prev = 3;
for (int i = 0; i < 4; i++)
{
rowa = ((uint2*)state)[0].x & 3; reduceDuplexRowtV3(prev, rowa, i, state, thread);
prev = i;
}
uint32_t shift = (memshift * rowa + 16 * 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];
//((vectype*)outputHash)[thread] = state[0];
} //thread
}
#if __CUDA_ARCH__ < 500
__global__ __launch_bounds__(64, 1)
#elif __CUDA_ARCH__ == 500
__global__ __launch_bounds__(32, 1)
#else
__global__ __launch_bounds__(TPB, 1)
#endif
void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
vectype state[4];
uint28 blake2b_IV[2]; uint28 blake2b_IV[2];
uint28 padding[2];
if (threadIdx.x == 0) { if (threadIdx.x == 0) {
((uint16*)blake2b_IV)[0] = make_uint16( ((uint16*)blake2b_IV)[0] = make_uint16(
0xf3bcc908, 0x6a09e667, 0x84caa73b, 0xbb67ae85, 0xf3bcc908, 0x6a09e667, 0x84caa73b, 0xbb67ae85,
0xfe94f82b, 0x3c6ef372, 0x5f1d36f1, 0xa54ff53a, 0xfe94f82b, 0x3c6ef372, 0x5f1d36f1, 0xa54ff53a,
0xade682d1, 0x510e527f, 0x2b3e6c1f, 0x9b05688c, 0xade682d1, 0x510e527f, 0x2b3e6c1f, 0x9b05688c,
0xfb41bd6b, 0x1f83d9ab, 0x137e2179, 0x5be0cd19 0xfb41bd6b, 0x1f83d9ab, 0x137e2179, 0x5be0cd19
);
((uint16*)padding)[0] = make_uint16(
0x20, 0x0, 0x20, 0x0, 0x20, 0x0, 0x01, 0x0,
0x04, 0x0, 0x04, 0x0, 0x80, 0x0, 0x0, 0x01000000
); );
} }
#if __CUDA_ARCH__ <= 350
if (thread < threads) if (thread < threads)
#endif
{ {
((uint2*)state)[0] = __ldg(&outputHash[thread]); ((uint2*)state)[0] = __ldg(&outputHash[thread]);
((uint2*)state)[1] = __ldg(&outputHash[thread + threads]); ((uint2*)state)[1] = __ldg(&outputHash[thread + threads]);
@ -444,61 +286,67 @@ void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHa
state[1] = state[0]; state[1] = state[0];
state[2] = shuffle4(((vectype*)blake2b_IV)[0], 0); state[2] = ((blake2b_IV)[0]);
state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0); state[3] = ((blake2b_IV)[1]);
for (int i = 0; i<12; i++) for (int i = 0; i<12; i++)
round_lyra_v35(state); round_lyra_v5(state);
state[0] ^= shuffle4(((vectype*)padding)[0], 0); ((uint2*)state)[0].x ^= 0x20;
state[1] ^= shuffle4(((vectype*)padding)[1], 0); ((uint2*)state)[1].x ^= 0x20;
((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_v35(state); round_lyra_v5(state);
uint32_t ps1 = (memshift * (Ncol - 1) + Nrow * Ncol * memshift * thread); uint32_t ps1 = (memshift * (Ncol - 1) + Nrow * Ncol * memshift * thread);
for (int i = 0; i < Ncol; i++) for (int i = 0; i < Ncol; i++)
{ {
uint32_t s1 = ps1 - memshift * i; const uint32_t s1 = ps1 - memshift * i;
for (int j = 0; j < 3; j++) DMatrix[s1] = state[0];
(DMatrix + s1)[j] = (state)[j]; DMatrix[s1+1] = state[1];
DMatrix[s1+2] = state[2];
round_lyra_v35(state); round_lyra_v5(state);
} }
reduceDuplex(state, thread); reduceDuplex50(state, thread);
reduceDuplexRowSetupV2(1, 0, 2, state, thread); reduceDuplexRowSetupV2(1, 0, 2, state, thread);
reduceDuplexRowSetupV2(2, 1, 3, state, thread); reduceDuplexRowSetupV2(2, 1, 3, state, thread);
uint32_t rowa; uint32_t rowa;
int prev=3; int prev=3;
for (int i = 0; i < 4; i++) { for (int i = 0; i < 4; i++)
{
rowa = ((uint2*)state)[0].x & 3; rowa = ((uint2*)state)[0].x & 3;
reduceDuplexRowtV2(prev, rowa, i, state, thread); reduceDuplexRowtV2(prev, rowa, i, state, thread);
prev=i; prev=i;
} }
uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread); const uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * memshift * thread);
#pragma unroll
for (int j = 0; j < 3; j++) for (int j = 0; j < 3; j++)
state[j] ^= __ldg4(&(DMatrix + shift)[j]); state[j] ^= __ldg4(&(DMatrix + shift)[j]);
for (int i = 0; i < 12; i++) for (int i = 0; i < 12; i++)
round_lyra_v35(state); round_lyra_v5(state);
outputHash[thread] = ((uint2*)state)[0]; outputHash[thread] = ((uint2*)state)[0];
outputHash[thread + threads] = ((uint2*)state)[1]; outputHash[thread + threads] = ((uint2*)state)[1];
outputHash[thread + 2 * threads] = ((uint2*)state)[2]; outputHash[thread + 2 * threads] = ((uint2*)state)[2];
outputHash[thread + 3 * threads] = ((uint2*)state)[3]; outputHash[thread + 3 * threads] = ((uint2*)state)[3];
} }
} }
#else #else
/* if __CUDA_ARCH__ < 300 .. */
__global__ void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {} __global__ void lyra2v2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {}
__global__ void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {}
#endif #endif
__host__ __host__
@ -512,12 +360,14 @@ __host__
void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order) void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order)
{ {
uint32_t tpb; uint32_t tpb;
if (device_sm[device_map[thr_id]] == 350) if (device_sm[device_map[thr_id]] < 350)
tpb = 64; tpb = TPB30;
else if (device_sm[device_map[thr_id]] == 350)
tpb = TPB35;
else if (device_sm[device_map[thr_id]] == 500) else if (device_sm[device_map[thr_id]] == 500)
tpb = 32; tpb = TPB50;
else else
tpb = TPB; tpb = TPB52;
dim3 grid((threads + tpb - 1) / tpb); dim3 grid((threads + tpb - 1) / tpb);
dim3 block(tpb); dim3 block(tpb);
@ -527,6 +377,5 @@ void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uin
else else
lyra2v2_gpu_hash_32_v3 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash); lyra2v2_gpu_hash_32_v3 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash);
MyStreamSynchronize(NULL, order, thr_id); //MyStreamSynchronize(NULL, order, thr_id);
} }

253
lyra2/cuda_lyra2v2_sm3.cuh
Unescape View File

@ -0,0 +1,253 @@
/* SM 3/3.5 Variant for lyra2REv2 */
#ifdef __INTELLISENSE__
/* just for vstudio code colors */
#undef __CUDA_ARCH__
#define __CUDA_ARCH__ 350
#endif
#define TPB30 16
#define TPB35 64
#if __CUDA_ARCH__ >= 300 && __CUDA_ARCH__ < 500
#define Nrow 4
#define Ncol 4
#define vectype ulonglong4
#define u64type uint64_t
#define memshift 4
__device__ vectype *DMatrix;
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);
}
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);
}
static __device__ __forceinline__
void reduceDuplexV3(vectype state[4], uint32_t thread)
{
vectype state1[3];
uint32_t ps1 = (Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * (Ncol - 1) * Nrow + memshift * 1 + Nrow * Ncol * memshift * thread);
#pragma unroll 4
for (int i = 0; i < Ncol; i++)
{
uint32_t s1 = ps1 + Nrow * i *memshift;
uint32_t s2 = ps2 - Nrow * 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 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 + Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
uint32_t ps3 = (Nrow * memshift * (Ncol - 1) + memshift * rowOut + Nrow * Ncol * memshift * thread);
for (int i = 0; i < Ncol; i++)
{
uint32_t s1 = ps1 + Nrow*i*memshift;
uint32_t s2 = ps2 + Nrow*i*memshift;
uint32_t s3 = ps3 - Nrow*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 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 + Nrow * Ncol * memshift * thread);
uint32_t ps2 = (memshift * rowInOut + Nrow * Ncol * memshift * thread);
uint32_t ps3 = (memshift * rowOut + Nrow * Ncol * memshift * thread);
#pragma nounroll
for (int i = 0; i < Ncol; i++)
{
uint32_t s1 = ps1 + Nrow * i*memshift;
uint32_t s2 = ps2 + Nrow * i*memshift;
uint32_t s3 = ps3 + Nrow * 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];
}
}
}
__global__ __launch_bounds__(TPB35, 1)
void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
vectype state[4];
uint28 blake2b_IV[2];
uint28 padding[2];
if (threadIdx.x == 0) {
((uint16*)blake2b_IV)[0] = make_uint16(
0xf3bcc908, 0x6a09e667 , 0x84caa73b, 0xbb67ae85 ,
0xfe94f82b, 0x3c6ef372 , 0x5f1d36f1, 0xa54ff53a ,
0xade682d1, 0x510e527f , 0x2b3e6c1f, 0x9b05688c ,
0xfb41bd6b, 0x1f83d9ab , 0x137e2179, 0x5be0cd19
);
((uint16*)padding)[0] = make_uint16(
0x20, 0x0 , 0x20, 0x0 , 0x20, 0x0 , 0x01, 0x0 ,
0x04, 0x0 , 0x04, 0x0 , 0x80, 0x0 , 0x0, 0x01000000
);
}
if (thread < threads)
{
((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] = shuffle4(((vectype*)blake2b_IV)[0], 0);
state[3] = shuffle4(((vectype*)blake2b_IV)[1], 0);
for (int i = 0; i<12; i++)
round_lyra_v35(state);
state[0] ^= shuffle4(((vectype*)padding)[0], 0);
state[1] ^= shuffle4(((vectype*)padding)[1], 0);
for (int i = 0; i<12; i++)
round_lyra_v35(state);
uint32_t ps1 = (4 * memshift * 3 + 16 * memshift * thread);
//#pragma unroll 4
for (int i = 0; i < 4; i++)
{
uint32_t s1 = ps1 - 4 * 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);
uint32_t rowa;
int prev = 3;
for (int i = 0; i < 4; i++)
{
rowa = ((uint2*)state)[0].x & 3; reduceDuplexRowtV3(prev, rowa, i, state, thread);
prev = i;
}
uint32_t shift = (memshift * rowa + 16 * 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
}
#else
/* if __CUDA_ARCH__ < 300 .. */
__global__ void lyra2v2_gpu_hash_32_v3(uint32_t threads, uint32_t startNounce, uint2 *outputHash) {}
#endif
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