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GOSTCoin CUDA miner project, compatible with most nvidia cards, containing only gostd algo
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ccminer-gostd-lite/x11/cuda_x11_simd512.cu

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/***************************************************************************************************
* SIMD512 SM3+ CUDA IMPLEMENTATION (require cuda_x11_simd512_func.cuh)
*/
#include "miner.h"
#include "cuda_helper.h"
#define TPB 128
uint32_t *d_state[MAX_GPUS];
uint4 *d_temp4[MAX_GPUS];
// texture bound to d_temp4[thr_id], for read access in Compaction kernel
texture<uint4, 1, cudaReadModeElementType> texRef1D_128;
#define DEVICE_DIRECT_CONSTANTS
#ifdef DEVICE_DIRECT_CONSTANTS
__constant__ uint8_t c_perm[8][8] = {
#else
__constant__ uint8_t c_perm[8][8];
const uint8_t h_perm[8][8] = {
#endif
{ 2, 3, 6, 7, 0, 1, 4, 5 },
{ 6, 7, 2, 3, 4, 5, 0, 1 },
{ 7, 6, 5, 4, 3, 2, 1, 0 },
{ 1, 0, 3, 2, 5, 4, 7, 6 },
{ 0, 1, 4, 5, 6, 7, 2, 3 },
{ 6, 7, 2, 3, 0, 1, 4, 5 },
{ 6, 7, 0, 1, 4, 5, 2, 3 },
{ 4, 5, 2, 3, 6, 7, 0, 1 }
};
/* used in cuda_x11_simd512_func.cuh (SIMD_Compress2) */
#ifdef DEVICE_DIRECT_CONSTANTS
__constant__ uint32_t c_IV_512[32] = {
#else
__constant__ uint32_t c_IV_512[32];
const uint32_t h_IV_512[32] = {
#endif
0x0ba16b95, 0x72f999ad, 0x9fecc2ae, 0xba3264fc, 0x5e894929, 0x8e9f30e5, 0x2f1daa37, 0xf0f2c558,
0xac506643, 0xa90635a5, 0xe25b878b, 0xaab7878f, 0x88817f7a, 0x0a02892b, 0x559a7550, 0x598f657e,
0x7eef60a1, 0x6b70e3e8, 0x9c1714d1, 0xb958e2a8, 0xab02675e, 0xed1c014f, 0xcd8d65bb, 0xfdb7a257,
0x09254899, 0xd699c7bc, 0x9019b6dc, 0x2b9022e4, 0x8fa14956, 0x21bf9bd3, 0xb94d0943, 0x6ffddc22
};
#ifdef DEVICE_DIRECT_CONSTANTS
__constant__ short c_FFT128_8_16_Twiddle[128] = {
#else
__constant__ short c_FFT128_8_16_Twiddle[128];
static const short h_FFT128_8_16_Twiddle[128] = {
#endif
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 60, 2, 120, 4, -17, 8, -34, 16, -68, 32, 121, 64, -15, 128, -30,
1, 46, 60, -67, 2, 92, 120, 123, 4, -73, -17, -11, 8, 111, -34, -22,
1, -67, 120, -73, 8, -22, -68, -70, 64, 81, -30, -46, -2,-123, 17,-111,
1,-118, 46, -31, 60, 116, -67, -61, 2, 21, 92, -62, 120, -25, 123,-122,
1, 116, 92,-122, -17, 84, -22, 18, 32, 114, 117, -49, -30, 118, 67, 62,
1, -31, -67, 21, 120, -122, -73, -50, 8, 9, -22, -89, -68, 52, -70, 114,
1, -61, 123, -50, -34, 18, -70, -99, 128, -98, 67, 25, 17, -9, 35, -79
};
#ifdef DEVICE_DIRECT_CONSTANTS
__constant__ short c_FFT256_2_128_Twiddle[128] = {
#else
__constant__ short c_FFT256_2_128_Twiddle[128];
static const short h_FFT256_2_128_Twiddle[128] = {
#endif
1, 41,-118, 45, 46, 87, -31, 14,
60,-110, 116,-127, -67, 80, -61, 69,
2, 82, 21, 90, 92, -83, -62, 28,
120, 37, -25, 3, 123, -97,-122,-119,
4, -93, 42, -77, -73, 91,-124, 56,
-17, 74, -50, 6, -11, 63, 13, 19,
8, 71, 84, 103, 111, -75, 9, 112,
-34,-109,-100, 12, -22, 126, 26, 38,
16,-115, -89, -51, -35, 107, 18, -33,
-68, 39, 57, 24, -44, -5, 52, 76,
32, 27, 79,-102, -70, -43, 36, -66,
121, 78, 114, 48, -88, -10, 104,-105,
64, 54, -99, 53, 117, -86, 72, 125,
-15,-101, -29, 96, 81, -20, -49, 47,
128, 108, 59, 106, -23, 85,-113, -7,
-30, 55, -58, -65, -95, -40, -98, 94
};
/************* the round function ****************/
#define IF(x, y, z) (((y ^ z) & x) ^ z)
#define MAJ(x, y, z) ((z &y) | ((z|y) & x))
#include "cuda_x11_simd512_sm2.cuh"
#include "cuda_x11_simd512_func.cuh"
#ifdef __INTELLISENSE__
/* just for vstudio code colors */
#define __CUDA_ARCH__ 500
#endif
#if __CUDA_ARCH__ >= 300
/********************* Message expansion ************************/
/*
* Reduce modulo 257; result is in [-127; 383]
* REDUCE(x) := (x&255) - (x>>8)
*/
#define REDUCE(x) \
(((x)&255) - ((x)>>8))
/*
* Reduce from [-127; 383] to [-128; 128]
* EXTRA_REDUCE_S(x) := x<=128 ? x : x-257
*/
#define EXTRA_REDUCE_S(x) \
((x)<=128 ? (x) : (x)-257)
/*
* Reduce modulo 257; result is in [-128; 128]
*/
#define REDUCE_FULL_S(x) \
EXTRA_REDUCE_S(REDUCE(x))
// Parallelization:
//
// FFT_8 wird 2 times 8-fach parallel ausgeführt (in FFT_64)
// and 1 time 16-fach parallel (in FFT_128_full)
//
// STEP8_IF and STEP8_MAJ beinhalten je 2x 8-fach parallel Operations
/**
* FFT_8 using w=4 as 8th root of unity
* Unrolled decimation in frequency (DIF) radix-2 NTT.
* Output data is in revbin_permuted order.
*/
__device__ __forceinline__
void FFT_8(int *y, int stripe)
{
#define X(i) y[stripe*i]
#define DO_REDUCE(i) \
X(i) = REDUCE(X(i))
#define DO_REDUCE_FULL_S(i) \
do { \
X(i) = REDUCE(X(i)); \
X(i) = EXTRA_REDUCE_S(X(i)); \
} while(0)
#define BUTTERFLY(i,j,n) \
do { \
int u= X(i); \
int v= X(j); \
X(i) = u+v; \
X(j) = (u-v) << (2*n); \
} while(0)
BUTTERFLY(0, 4, 0);
BUTTERFLY(1, 5, 1);
BUTTERFLY(2, 6, 2);
BUTTERFLY(3, 7, 3);
DO_REDUCE(6);
DO_REDUCE(7);
BUTTERFLY(0, 2, 0);
BUTTERFLY(4, 6, 0);
BUTTERFLY(1, 3, 2);
BUTTERFLY(5, 7, 2);
DO_REDUCE(7);
BUTTERFLY(0, 1, 0);
BUTTERFLY(2, 3, 0);
BUTTERFLY(4, 5, 0);
BUTTERFLY(6, 7, 0);
DO_REDUCE_FULL_S(0);
DO_REDUCE_FULL_S(1);
DO_REDUCE_FULL_S(2);
DO_REDUCE_FULL_S(3);
DO_REDUCE_FULL_S(4);
DO_REDUCE_FULL_S(5);
DO_REDUCE_FULL_S(6);
DO_REDUCE_FULL_S(7);
#undef X
#undef DO_REDUCE
#undef DO_REDUCE_FULL_S
#undef BUTTERFLY
}
#if defined(__CUDA_ARCH__)
#if __CUDA_ARCH__ < 300
#define __shfl(var, srcLane, width) (uint32_t)(var)
// #error __shfl() not supported by SM 2.x
#endif
#endif
/**
* FFT_16 using w=2 as 16th root of unity
* Unrolled decimation in frequency (DIF) radix-2 NTT.
* Output data is in revbin_permuted order.
*/
__device__ __forceinline__
void FFT_16(int *y)
{
#define DO_REDUCE_FULL_S(i) \
do { \
y[i] = REDUCE(y[i]); \
y[i] = EXTRA_REDUCE_S(y[i]); \
} while(0)
int u,v;
// BUTTERFLY(0, 8, 0);
// BUTTERFLY(1, 9, 1);
// BUTTERFLY(2, 10, 2);
// BUTTERFLY(3, 11, 3);
// BUTTERFLY(4, 12, 4);
// BUTTERFLY(5, 13, 5);
// BUTTERFLY(6, 14, 6);
// BUTTERFLY(7, 15, 7);
{
u = y[0]; // 0..7
v = y[1]; // 8..15
y[0] = u+v;
y[1] = (u-v) << (threadIdx.x&7);
}
// DO_REDUCE(11);
// DO_REDUCE(12);
// DO_REDUCE(13);
// DO_REDUCE(14);
// DO_REDUCE(15);
if ((threadIdx.x&7) >=3) y[1] = REDUCE(y[1]); // 11...15
// BUTTERFLY( 0, 4, 0);
// BUTTERFLY( 1, 5, 2);
// BUTTERFLY( 2, 6, 4);
// BUTTERFLY( 3, 7, 6);
{
u = __shfl((int)y[0], (threadIdx.x&3),8); // 0,1,2,3 0,1,2,3
v = __shfl((int)y[0],4+(threadIdx.x&3),8); // 4,5,6,7 4,5,6,7
y[0] = ((threadIdx.x&7) < 4) ? (u+v) : ((u-v) << (2*(threadIdx.x&3)));
}
// BUTTERFLY( 8, 12, 0);
// BUTTERFLY( 9, 13, 2);
// BUTTERFLY(10, 14, 4);
// BUTTERFLY(11, 15, 6);
{
u = __shfl((int)y[1], (threadIdx.x&3),8); // 8,9,10,11 8,9,10,11
v = __shfl((int)y[1],4+(threadIdx.x&3),8); // 12,13,14,15 12,13,14,15
y[1] = ((threadIdx.x&7) < 4) ? (u+v) : ((u-v) << (2*(threadIdx.x&3)));
}
// DO_REDUCE(5);
// DO_REDUCE(7);
// DO_REDUCE(13);
// DO_REDUCE(15);
if ((threadIdx.x&1) && (threadIdx.x&7) >= 4) {
y[0] = REDUCE(y[0]); // 5, 7
y[1] = REDUCE(y[1]); // 13, 15
}
// BUTTERFLY( 0, 2, 0);
// BUTTERFLY( 1, 3, 4);
// BUTTERFLY( 4, 6, 0);
// BUTTERFLY( 5, 7, 4);
{
u = __shfl((int)y[0], (threadIdx.x&5),8); // 0,1,0,1 4,5,4,5
v = __shfl((int)y[0],2+(threadIdx.x&5),8); // 2,3,2,3 6,7,6,7
y[0] = ((threadIdx.x&3) < 2) ? (u+v) : ((u-v) << (4*(threadIdx.x&1)));
}
// BUTTERFLY( 8, 10, 0);
// BUTTERFLY( 9, 11, 4);
// BUTTERFLY(12, 14, 0);
// BUTTERFLY(13, 15, 4);
{
u = __shfl((int)y[1], (threadIdx.x&5),8); // 8,9,8,9 12,13,12,13
v = __shfl((int)y[1],2+(threadIdx.x&5),8); // 10,11,10,11 14,15,14,15
y[1] = ((threadIdx.x&3) < 2) ? (u+v) : ((u-v) << (4*(threadIdx.x&1)));
}
// BUTTERFLY( 0, 1, 0);
// BUTTERFLY( 2, 3, 0);
// BUTTERFLY( 4, 5, 0);
// BUTTERFLY( 6, 7, 0);
{
u = __shfl((int)y[0], (threadIdx.x&6),8); // 0,0,2,2 4,4,6,6
v = __shfl((int)y[0],1+(threadIdx.x&6),8); // 1,1,3,3 5,5,7,7
y[0] = ((threadIdx.x&1) < 1) ? (u+v) : (u-v);
}
// BUTTERFLY( 8, 9, 0);
// BUTTERFLY(10, 11, 0);
// BUTTERFLY(12, 13, 0);
// BUTTERFLY(14, 15, 0);
{
u = __shfl((int)y[1], (threadIdx.x&6),8); // 8,8,10,10 12,12,14,14
v = __shfl((int)y[1],1+(threadIdx.x&6),8); // 9,9,11,11 13,13,15,15
y[1] = ((threadIdx.x&1) < 1) ? (u+v) : (u-v);
}
DO_REDUCE_FULL_S( 0); // 0...7
DO_REDUCE_FULL_S( 1); // 8...15
#undef DO_REDUCE_FULL_S
}
__device__ __forceinline__
void FFT_128_full(int y[128])
{
int i;
FFT_8(y+0,2); // eight parallel FFT8's
FFT_8(y+1,2); // eight parallel FFT8's
#pragma unroll 16
for (i=0; i<16; i++)
/*if (i & 7)*/ y[i] = REDUCE(y[i]*c_FFT128_8_16_Twiddle[i*8+(threadIdx.x&7)]);
#pragma unroll 8
for (i=0; i<8; i++)
FFT_16(y+2*i); // eight sequential FFT16's, each one executed in parallel by 8 threads
}
__device__ __forceinline__
void FFT_256_halfzero(int y[256])
{
/*
* FFT_256 using w=41 as 256th root of unity.
* Decimation in frequency (DIF) NTT.
* Output data is in revbin_permuted order.
* In place.
*/
const int tmp = y[15];
#pragma unroll 8
for (int i=0; i<8; i++)
y[16+i] = REDUCE(y[i] * c_FFT256_2_128_Twiddle[8*i+(threadIdx.x&7)]);
#pragma unroll 8
for (int i=8; i<16; i++)
y[16+i] = 0;
/* handle X^255 with an additional butterfly */
if ((threadIdx.x&7) == 7)
{
y[15] = REDUCE(tmp + 1);
y[31] = REDUCE((tmp - 1) * c_FFT256_2_128_Twiddle[127]);
}
FFT_128_full(y);
FFT_128_full(y+16);
}
/***************************************************/
__device__ __forceinline__
void Expansion(const uint32_t *data, uint4 *g_temp4)
{
/* Message Expansion using Number Theoretical Transform similar to FFT */
int expanded[32];
#pragma unroll 4
for (int i=0; i < 4; i++) {
expanded[ i] = __byte_perm(__shfl((int)data[0], 2*i, 8), __shfl((int)data[0], (2*i)+1, 8), threadIdx.x&7)&0xff;
expanded[4+i] = __byte_perm(__shfl((int)data[1], 2*i, 8), __shfl((int)data[1], (2*i)+1, 8), threadIdx.x&7)&0xff;
}
#pragma unroll 8
for (int i=8; i < 16; i++)
expanded[i] = 0;
FFT_256_halfzero(expanded);
// store w matrices in global memory
#define mul_185(x) ( (x)*185 )
#define mul_233(x) ( (x)*233 )
uint4 vec0;
int P, Q, P1, Q1, P2, Q2;
bool even = (threadIdx.x & 1) == 0;
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
// 2 6 0 4
P1 = expanded[ 0]; P2 = __shfl(expanded[ 2], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[16]; Q2 = __shfl(expanded[18], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[0][threadIdx.x&7], 8);
P1 = expanded[ 8]; P2 = __shfl(expanded[10], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[24]; Q2 = __shfl(expanded[26], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[0][threadIdx.x&7], 8);
P1 = expanded[ 4]; P2 = __shfl(expanded[ 6], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[20]; Q2 = __shfl(expanded[22], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[0][threadIdx.x&7], 8);
P1 = expanded[12]; P2 = __shfl(expanded[14], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[28]; Q2 = __shfl(expanded[30], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[0][threadIdx.x&7], 8);
g_temp4[threadIdx.x&7] = vec0;
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
// 6 2 4 0
P1 = expanded[ 1]; P2 = __shfl(expanded[ 3], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[17]; Q2 = __shfl(expanded[19], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[1][threadIdx.x&7], 8);
P1 = expanded[ 9]; P2 = __shfl(expanded[11], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[25]; Q2 = __shfl(expanded[27], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[1][threadIdx.x&7], 8);
P1 = expanded[ 5]; P2 = __shfl(expanded[ 7], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[21]; Q2 = __shfl(expanded[23], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[1][threadIdx.x&7], 8);
P1 = expanded[13]; P2 = __shfl(expanded[15], (threadIdx.x-1)&7, 8); P = even ? P1 : P2;
Q1 = expanded[29]; Q2 = __shfl(expanded[31], (threadIdx.x-1)&7, 8); Q = even ? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[1][threadIdx.x&7], 8);
g_temp4[8+(threadIdx.x&7)] = vec0;
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
// 7 5 3 1
bool hi = (threadIdx.x&7)>=4;
P1 = hi?expanded[ 1]:expanded[ 0]; P2 = __shfl(hi?expanded[ 3]:expanded[ 2], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = hi?expanded[17]:expanded[16]; Q2 = __shfl(hi?expanded[19]:expanded[18], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[2][threadIdx.x&7], 8);
P1 = hi?expanded[ 9]:expanded[ 8]; P2 = __shfl(hi?expanded[11]:expanded[10], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = hi?expanded[25]:expanded[24]; Q2 = __shfl(hi?expanded[27]:expanded[26], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[2][threadIdx.x&7], 8);
P1 = hi?expanded[ 5]:expanded[ 4]; P2 = __shfl(hi?expanded[ 7]:expanded[ 6], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = hi?expanded[21]:expanded[20]; Q2 = __shfl(hi?expanded[23]:expanded[22], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[2][threadIdx.x&7], 8);
P1 = hi?expanded[13]:expanded[12]; P2 = __shfl(hi?expanded[15]:expanded[14], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = hi?expanded[29]:expanded[28]; Q2 = __shfl(hi?expanded[31]:expanded[30], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[2][threadIdx.x&7], 8);
g_temp4[16+(threadIdx.x&7)] = vec0;
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
// 1 9 5 13 3 11 7 15 17 25 21 29 19 27 23 31 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
// 0 8 4 12 2 10 6 14 16 24 20 28 18 26 22 30 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
// 1 3 5 7
bool lo = (threadIdx.x&7)<4;
P1 = lo?expanded[ 1]:expanded[ 0]; P2 = __shfl(lo?expanded[ 3]:expanded[ 2], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = lo?expanded[17]:expanded[16]; Q2 = __shfl(lo?expanded[19]:expanded[18], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[3][threadIdx.x&7], 8);
P1 = lo?expanded[ 9]:expanded[ 8]; P2 = __shfl(lo?expanded[11]:expanded[10], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = lo?expanded[25]:expanded[24]; Q2 = __shfl(lo?expanded[27]:expanded[26], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[3][threadIdx.x&7], 8);
P1 = lo?expanded[ 5]:expanded[ 4]; P2 = __shfl(lo?expanded[ 7]:expanded[ 6], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = lo?expanded[21]:expanded[20]; Q2 = __shfl(lo?expanded[23]:expanded[22], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[3][threadIdx.x&7], 8);
P1 = lo?expanded[13]:expanded[12]; P2 = __shfl(lo?expanded[15]:expanded[14], (threadIdx.x+1)&7, 8); P = !even ? P1 : P2;
Q1 = lo?expanded[29]:expanded[28]; Q2 = __shfl(lo?expanded[31]:expanded[30], (threadIdx.x+1)&7, 8); Q = !even ? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_185(P), mul_185(Q) , 0x5410), c_perm[3][threadIdx.x&7], 8);
g_temp4[24+(threadIdx.x&7)] = vec0;
// 1 9 5 13 3 11 7 15 1 9 5 13 3 11 7 15 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
// 0 8 4 12 2 10 6 14 0 8 4 12 2 10 6 14 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5
// 1 9 5 13 3 11 7 15 1 9 5 13 3 11 7 15 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7
// 0 8 4 12 2 10 6 14 0 8 4 12 2 10 6 14 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
//{ 8, 72, 40, 104, 24, 88, 56, 120 }, { 9, 73, 41, 105, 25, 89, 57, 121 },
//{ 4, 68, 36, 100, 20, 84, 52, 116 }, { 5, 69, 37, 101, 21, 85, 53, 117 },
//{ 14, 78, 46, 110, 30, 94, 62, 126 }, { 15, 79, 47, 111, 31, 95, 63, 127 },
//{ 2, 66, 34, 98, 18, 82, 50, 114 }, { 3, 67, 35, 99, 19, 83, 51, 115 },
bool sel = ((threadIdx.x+2)&7) >= 4; // 2,3,4,5
P1 = sel?expanded[0]:expanded[1]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[2]:expanded[3]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[4][threadIdx.x&7], 8);
P1 = sel?expanded[8]:expanded[9]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[10]:expanded[11]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[4][threadIdx.x&7], 8);
P1 = sel?expanded[4]:expanded[5]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[6]:expanded[7]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[4][threadIdx.x&7], 8);
P1 = sel?expanded[12]:expanded[13]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[14]:expanded[15]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[4][threadIdx.x&7], 8);
g_temp4[32+(threadIdx.x&7)] = vec0;
// 0 8 4 12 2 10 6 14 0 8 4 12 2 10 6 14 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7
// 1 9 5 13 3 11 7 15 1 9 5 13 3 11 7 15 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
// 0 8 4 12 2 10 6 14 0 8 4 12 2 10 6 14 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
// 1 9 5 13 3 11 7 15 1 9 5 13 3 11 7 15 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5
P1 = sel?expanded[1]:expanded[0]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[3]:expanded[2]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[5][threadIdx.x&7], 8);
P1 = sel?expanded[9]:expanded[8]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[11]:expanded[10]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[5][threadIdx.x&7], 8);
P1 = sel?expanded[5]:expanded[4]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[7]:expanded[6]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[5][threadIdx.x&7], 8);
P1 = sel?expanded[13]:expanded[12]; Q1 = __shfl(P1, threadIdx.x^1, 8);
Q2 = sel?expanded[15]:expanded[14]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[5][threadIdx.x&7], 8);
g_temp4[40+(threadIdx.x&7)] = vec0;
// 16 24 20 28 18 26 22 30 16 24 20 28 18 26 22 30 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7
// 16 24 20 28 18 26 22 30 16 24 20 28 18 26 22 30 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
// 17 25 21 29 19 27 23 31 17 25 21 29 19 27 23 31 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
// 17 25 21 29 19 27 23 31 17 25 21 29 19 27 23 31 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7
// sel markiert threads 2,3,4,5
int t;
t = __shfl(expanded[17],(threadIdx.x+4)&7,8); P1 = sel?t:expanded[16]; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[19],(threadIdx.x+4)&7,8); Q2 = sel?t:expanded[18]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[6][threadIdx.x&7], 8);
t = __shfl(expanded[25],(threadIdx.x+4)&7,8); P1 = sel?t:expanded[24]; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[27],(threadIdx.x+4)&7,8); Q2 = sel?t:expanded[26]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[6][threadIdx.x&7], 8);
t = __shfl(expanded[21],(threadIdx.x+4)&7,8); P1 = sel?t:expanded[20]; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[23],(threadIdx.x+4)&7,8); Q2 = sel?t:expanded[22]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[6][threadIdx.x&7], 8);
t = __shfl(expanded[29],(threadIdx.x+4)&7,8); P1 = sel?t:expanded[28]; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[31],(threadIdx.x+4)&7,8); Q2 = sel?t:expanded[30]; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[6][threadIdx.x&7], 8);
g_temp4[48+(threadIdx.x&7)] = vec0;
// 17 25 21 29 19 27 23 31 17 25 21 29 19 27 23 31 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5
// 17 25 21 29 19 27 23 31 17 25 21 29 19 27 23 31 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
// 16 24 20 28 18 26 22 30 16 24 20 28 18 26 22 30 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3
// 16 24 20 28 18 26 22 30 16 24 20 28 18 26 22 30 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5
// sel markiert threads 2,3,4,5
t = __shfl(expanded[16],(threadIdx.x+4)&7,8); P1 = sel?expanded[17]:t; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[18],(threadIdx.x+4)&7,8); Q2 = sel?expanded[19]:t; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.x = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[7][threadIdx.x&7], 8);
t = __shfl(expanded[24],(threadIdx.x+4)&7,8); P1 = sel?expanded[25]:t; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[26],(threadIdx.x+4)&7,8); Q2 = sel?expanded[27]:t; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.y = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[7][threadIdx.x&7], 8);
t = __shfl(expanded[20],(threadIdx.x+4)&7,8); P1 = sel?expanded[21]:t; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[22],(threadIdx.x+4)&7,8); Q2 = sel?expanded[23]:t; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.z = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[7][threadIdx.x&7], 8);
t = __shfl(expanded[28],(threadIdx.x+4)&7,8); P1 = sel?expanded[29]:t; Q1 = __shfl(P1, threadIdx.x^1, 8);
t = __shfl(expanded[30],(threadIdx.x+4)&7,8); Q2 = sel?expanded[31]:t; P2 = __shfl(Q2, threadIdx.x^1, 8);
P = even? P1 : P2; Q = even? Q1 : Q2;
vec0.w = __shfl((int)__byte_perm(mul_233(P), mul_233(Q) , 0x5410), c_perm[7][threadIdx.x&7], 8);
g_temp4[56+(threadIdx.x&7)] = vec0;
#undef mul_185
#undef mul_233
}
/***************************************************/
__global__ __launch_bounds__(TPB, 4)
void x11_simd512_gpu_expand_64(uint32_t threads, uint32_t *g_hash, uint4 *g_temp4)
{
int threadBloc = (blockDim.x * blockIdx.x + threadIdx.x) / 8;
if (threadBloc < threads)
{
int hashPosition = threadBloc * 16;
uint32_t *inpHash = &g_hash[hashPosition];
// Read hash per 8 threads
uint32_t Hash[2];
int ndx = threadIdx.x & 7;
Hash[0] = inpHash[ndx];
Hash[1] = inpHash[ndx + 8];
// Puffer für expandierte Nachricht
uint4 *temp4 = &g_temp4[hashPosition * 4];
Expansion(Hash, temp4);
}
}
__global__ __launch_bounds__(TPB, 1)
void x11_simd512_gpu_compress1_64(uint32_t threads, uint32_t *g_hash, uint4 *g_fft4, uint32_t *g_state)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
uint32_t *Hash = &g_hash[thread * 16];
Compression1(Hash, thread, g_fft4, g_state);
}
}
__global__ __launch_bounds__(TPB, 1)
void x11_simd512_gpu_compress2_64(uint32_t threads, uint4 *g_fft4, uint32_t *g_state)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
Compression2(thread, g_fft4, g_state);
}
}
__global__ __launch_bounds__(TPB, 2)
void x11_simd512_gpu_compress_64_maxwell(uint32_t threads, uint32_t *g_hash, uint4 *g_fft4, uint32_t *g_state)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
uint32_t *Hash = &g_hash[thread * 16];
Compression1(Hash, thread, g_fft4, g_state);
Compression2(thread, g_fft4, g_state);
}
}
__global__ __launch_bounds__(TPB, 2)
void x11_simd512_gpu_final_64(uint32_t threads, uint32_t *g_hash, uint4 *g_fft4, uint32_t *g_state)
{
uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
uint32_t *Hash = &g_hash[thread * 16];
Final(Hash, thread, g_fft4, g_state);
}
}
#else
__global__ void x11_simd512_gpu_expand_64(uint32_t threads, uint32_t *g_hash, uint4 *g_temp4) {}
__global__ void x11_simd512_gpu_compress1_64(uint32_t threads, uint32_t *g_hash, uint4 *g_fft4, uint32_t *g_state) {}
__global__ void x11_simd512_gpu_compress2_64(uint32_t threads, uint4 *g_fft4, uint32_t *g_state) {}
__global__ void x11_simd512_gpu_compress_64_maxwell(uint32_t threads, uint32_t *g_hash, uint4 *g_fft4, uint32_t *g_state) {}
__global__ void x11_simd512_gpu_final_64(uint32_t threads, uint32_t *g_hash, uint4 *g_fft4, uint32_t *g_state) {}
#endif /* SM3+ */
__host__
int x11_simd512_cpu_init(int thr_id, uint32_t threads)
{
int dev_id = device_map[thr_id];
cuda_get_arch(thr_id);
if (device_sm[dev_id] < 300 || cuda_arch[dev_id] < 300) {
x11_simd512_cpu_init_sm2(thr_id);
return 0;
}
CUDA_CALL_OR_RET_X(cudaMalloc(&d_temp4[thr_id], 64*sizeof(uint4)*threads), (int) err); /* todo: prevent -i 21 */
CUDA_CALL_OR_RET_X(cudaMalloc(&d_state[thr_id], 32*sizeof(int)*threads), (int) err);
#ifndef DEVICE_DIRECT_CONSTANTS
cudaMemcpyToSymbol(c_perm, h_perm, sizeof(h_perm), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(c_IV_512, h_IV_512, sizeof(h_IV_512), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(c_FFT128_8_16_Twiddle, h_FFT128_8_16_Twiddle, sizeof(h_FFT128_8_16_Twiddle), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(c_FFT256_2_128_Twiddle, h_FFT256_2_128_Twiddle, sizeof(h_FFT256_2_128_Twiddle), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(d_cw0, h_cw0, sizeof(h_cw0), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(d_cw1, h_cw1, sizeof(h_cw1), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(d_cw2, h_cw2, sizeof(h_cw2), 0, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(d_cw3, h_cw3, sizeof(h_cw3), 0, cudaMemcpyHostToDevice);
#endif
// Texture for 128-Bit Zugriffe
cudaChannelFormatDesc channelDesc128 = cudaCreateChannelDesc<uint4>();
texRef1D_128.normalized = 0;
texRef1D_128.filterMode = cudaFilterModePoint;
texRef1D_128.addressMode[0] = cudaAddressModeClamp;
CUDA_CALL_OR_RET_X(cudaBindTexture(NULL, &texRef1D_128, d_temp4[thr_id], &channelDesc128, 64*sizeof(uint4)*threads), (int) err);
return 0;
}
__host__
void x11_simd512_cpu_free(int thr_id)
{
int dev_id = device_map[thr_id];
if (device_sm[dev_id] >= 300 && cuda_arch[dev_id] >= 300) {
cudaFree(d_temp4[thr_id]);
cudaFree(d_state[thr_id]);
}
}
__host__
void x11_simd512_cpu_hash_64(int thr_id, uint32_t threads, uint32_t startNounce, uint32_t *d_nonceVector, uint32_t *d_hash, int order)
{
const uint32_t threadsperblock = TPB;
int dev_id = device_map[thr_id];
dim3 block(threadsperblock);
dim3 grid((threads + threadsperblock-1) / threadsperblock);
dim3 gridX8(grid.x * 8);
if (d_nonceVector != NULL || device_sm[dev_id] < 300 || cuda_arch[dev_id] < 300) {
x11_simd512_cpu_hash_64_sm2(thr_id, threads, startNounce, d_nonceVector, d_hash, order);
return;
}
x11_simd512_gpu_expand_64 <<<gridX8, block>>> (threads, d_hash, d_temp4[thr_id]);
if (device_sm[dev_id] >= 500 && cuda_arch[dev_id] >= 500) {
x11_simd512_gpu_compress_64_maxwell <<< grid, block >>> (threads, d_hash, d_temp4[thr_id], d_state[thr_id]);
} else {
x11_simd512_gpu_compress1_64 <<< grid, block >>> (threads, d_hash, d_temp4[thr_id], d_state[thr_id]);
x11_simd512_gpu_compress2_64 <<< grid, block >>> (threads, d_temp4[thr_id], d_state[thr_id]);
}
x11_simd512_gpu_final_64 <<<grid, block>>> (threads, d_hash, d_temp4[thr_id], d_state[thr_id]);
//MyStreamSynchronize(NULL, order, thr_id);
}