lyra2: import latest nanashi code (v1)

README.txt

@@ -247,6 +247,7 @@ features.
   Aug. 10th 2016  v1.8.1
                   SIA Blake2-B Algo (getwork over stratum for Suprnova)
                   SIA Nanopool RPC (getwork over http)
+                  Update also the older lyra2 with Nanashi version
 
   July 20th 2016  v1.8.0
                   Pascal support with cuda 8

lyra2/cuda_lyra2.cu

@@ -1,40 +1,214 @@
 /**
- * Lyra2 (v1) cuda implementation based on djm34 work - SM 5/5.2
- * tpruvot@github 2015
+ * Lyra2 (v1) cuda implementation based on djm34 work
+ * tpruvot@github 2015, Nanashi 08/2016 (from 1.8-r2)
  */
 
 #include <stdio.h>
 #include <memory.h>
 
-#define TPB50 16
-#define TPB52 8
+#define TPB52 32
 
 #include "cuda_lyra2_sm2.cuh"
+#include "cuda_lyra2_sm5.cuh"
 
 #ifdef __INTELLISENSE__
 /* just for vstudio code colors */
-#define __CUDA_ARCH__ 500
+#define __CUDA_ARCH__ 520
 #endif
 
-#if !defined(__CUDA_ARCH__) ||  __CUDA_ARCH__ >= 500
+#if !defined(__CUDA_ARCH__) ||  __CUDA_ARCH__ > 500
 
-#include "cuda_vector_uint2x4.h"
+#include "cuda_lyra2_vectors.h"
 
-#define memshift 3
+#ifdef __INTELLISENSE__
+/* just for vstudio code colors */
+__device__ uint32_t __shfl(uint32_t a, uint32_t b, uint32_t c);
+#endif
 
+#define Nrow 8
 #define Ncol 8
-#define NcolMask 0x7
+#define memshift 3
+
+#define BUF_COUNT 0
+
+__device__ uint2 *DMatrix;
+
+__device__ __forceinline__ void LD4S(uint2 res[3], const int row, const int col, const int thread, const int threads)
+{
+#if BUF_COUNT != 8
+	extern __shared__ uint2 shared_mem[];
+	const int s0 = (Ncol * (row - BUF_COUNT) + col) * memshift;
+#endif
+#if BUF_COUNT != 0
+	const int d0 = (memshift *(Ncol * row + col) * threads + thread)*blockDim.x + threadIdx.x;
+#endif
+
+#if BUF_COUNT == 8
+	#pragma unroll
+	for (int j = 0; j < 3; j++)
+		res[j] = *(DMatrix + d0 + j * threads * blockDim.x);
+#elif BUF_COUNT == 0
+	#pragma unroll
+	for (int j = 0; j < 3; j++)
+		res[j] = shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x];
+#else
+	if (row < BUF_COUNT)
+	{
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			res[j] = *(DMatrix + d0 + j * threads * blockDim.x);
+	}
+	else
+	{
+	#pragma unroll
+		for (int j = 0; j < 3; j++)
+			res[j] = shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x];
+	}
+#endif
+}
+
+__device__ __forceinline__ void ST4S(const int row, const int col, const uint2 data[3], const int thread, const int threads)
+{
+#if BUF_COUNT != 8
+	extern __shared__ uint2 shared_mem[];
+	const int s0 = (Ncol * (row - BUF_COUNT) + col) * memshift;
+#endif
+#if BUF_COUNT != 0
+	const int d0 = (memshift *(Ncol * row + col) * threads + thread)*blockDim.x + threadIdx.x;
+#endif
+
+#if BUF_COUNT == 8
+	#pragma unroll
+	for (int j = 0; j < 3; j++)
+		*(DMatrix + d0 + j * threads * blockDim.x) = data[j];
+
+#elif BUF_COUNT == 0
+	#pragma unroll
+	for (int j = 0; j < 3; j++)
+		shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data[j];
+
+#else
+	if (row < BUF_COUNT)
+	{
+	#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + d0 + j * threads * blockDim.x) = data[j];
+	}
+	else
+	{
+	#pragma unroll
+		for (int j = 0; j < 3; j++)
+			shared_mem[((s0 + j) * blockDim.y + threadIdx.y) * blockDim.x + threadIdx.x] = data[j];
+	}
+#endif
+}
+
+#if __CUDA_ARCH__ >= 300
+__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
+{
+	return __shfl(a, b, c);
+}
 
-__device__ uint2x4* DMatrix;
+__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
+{
+	return make_uint2(__shfl(a.x, b, c), __shfl(a.y, b, c));
+}
+
+__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
+{
+	a1 = WarpShuffle(a1, b1, c);
+	a2 = WarpShuffle(a2, b2, c);
+	a3 = WarpShuffle(a3, b3, c);
+}
+
+#else
+__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
+{
+	extern __shared__ uint2 shared_mem[];
+
+	const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
+	uint32_t *_ptr = (uint32_t*)shared_mem;
+
+	__threadfence_block();
+	uint32_t buf = _ptr[thread];
+
+	_ptr[thread] = a;
+	__threadfence_block();
+	uint32_t result = _ptr[(thread&~(c - 1)) + (b&(c - 1))];
+
+	__threadfence_block();
+	_ptr[thread] = buf;
+
+	__threadfence_block();
+	return result;
+}
+
+__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
+{
+	extern __shared__ uint2 shared_mem[];
+
+	const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
+
+	__threadfence_block();
+	uint2 buf = shared_mem[thread];
+
+	shared_mem[thread] = a;
+	__threadfence_block();
+	uint2 result = shared_mem[(thread&~(c - 1)) + (b&(c - 1))];
+
+	__threadfence_block();
+	shared_mem[thread] = buf;
+
+	__threadfence_block();
+	return result;
+}
 
+__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
+{
+	extern __shared__ uint2 shared_mem[];
+
+	const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
+
+	__threadfence_block();
+	uint2 buf = shared_mem[thread];
+
+	shared_mem[thread] = a1;
+	__threadfence_block();
+	a1 = shared_mem[(thread&~(c - 1)) + (b1&(c - 1))];
+	__threadfence_block();
+	shared_mem[thread] = a2;
+	__threadfence_block();
+	a2 = shared_mem[(thread&~(c - 1)) + (b2&(c - 1))];
+	__threadfence_block();
+	shared_mem[thread] = a3;
+	__threadfence_block();
+	a3 = shared_mem[(thread&~(c - 1)) + (b3&(c - 1))];
+
+	__threadfence_block();
+	shared_mem[thread] = buf;
+	__threadfence_block();
+}
+
+#endif
+
+#if __CUDA_ARCH__ > 500 || !defined(__CUDA_ARCH)
 static __device__ __forceinline__
 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);
-	a += b; d ^= a; d = ROR2(d, 16);
+	a += b; uint2 tmp = d; d.y = a.x ^ tmp.x; d.x = a.y ^ tmp.y;
+	c += d; b ^= c; b = ROR24(b);
+	a += b; d ^= a; d = ROR16(d);
 	c += d; b ^= c; b = ROR2(b, 63);
 }
+#endif
+
+__device__ __forceinline__ void round_lyra(uint2 s[4])
+{
+	Gfunc(s[0], s[1], s[2], s[3]);
+	WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 1, threadIdx.x + 2, threadIdx.x + 3, 4);
+	Gfunc(s[0], s[1], s[2], s[3]);
+	WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 3, threadIdx.x + 2, threadIdx.x + 1, 4);
+}
 
 static __device__ __forceinline__
 void round_lyra(uint2x4* s)
@@ -50,21 +224,24 @@ void round_lyra(uint2x4* s)
 }
 
 static __device__ __forceinline__
-void reduceDuplex(uint2x4 state[4], uint32_t thread)
+void reduceDuplex(uint2 state[4], uint32_t thread, const uint32_t threads)
 {
-	uint2x4 state1[3];
+	uint2 state1[3];
 
-	const uint32_t ps1 = (256 * thread);
-	const uint32_t ps2 = (memshift * 7 + memshift * 8 + 256 * thread);
+#if __CUDA_ARCH__ > 500
+#pragma unroll
+#endif
+	for (int i = 0; i < Nrow; i++)
+	{
+		ST4S(0, Ncol - i - 1, state, thread, threads);
+
+		round_lyra(state);
+	}
 
 	#pragma unroll 4
-	for (int i = 0; i < 8; i++)
+	for (int i = 0; i < Nrow; 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]);
+		LD4S(state1, 0, i, thread, threads);
 		for (int j = 0; j < 3; j++)
 			state[j] ^= state1[j];
 
@@ -72,208 +249,324 @@ void reduceDuplex(uint2x4 state[4], uint32_t thread)
 
 		for (int j = 0; j < 3; j++)
 			state1[j] ^= state[j];
-		for (int j = 0; j < 3; j++)
-			(DMatrix + s2)[j] = state1[j];
+		ST4S(1, Ncol - i - 1, state1, thread, threads);
 	}
 }
 
 static __device__ __forceinline__
-void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2x4 state[4], uint32_t thread)
+void reduceDuplexRowSetup(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], uint32_t thread, const uint32_t threads)
 {
-	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);
+	uint2 state1[3], state2[3];
 
 	#pragma unroll 1
-	for (int i = 0; i < 8; i++)
+	for (int i = 0; i < Nrow; i++)
 	{
-		const uint32_t s1 = ps1 + i*memshift;
-		const uint32_t s2 = ps2 + i*memshift;
+		LD4S(state1, rowIn, i, thread, threads);
+		LD4S(state2, rowInOut, i, thread, threads);
 		for (int j = 0; j < 3; j++)
-			state1[j]= __ldg4(&(DMatrix + s1)[j]);
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
 		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;
+			state1[j] ^= state[j];
+
+		ST4S(rowOut, Ncol - i - 1, state1, thread, threads);
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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;
 		}
 
+		ST4S(rowInOut, i, state2, thread, threads);
+	}
+}
+
+static __device__ __forceinline__
+void reduceDuplexRowt(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
+{
+	for (int i = 0; i < Nrow; i++)
+	{
+		uint2 state1[3], state2[3];
+
+		LD4S(state1, rowIn, i, thread, threads);
+		LD4S(state2, rowInOut, i, thread, threads);
+
+#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
 		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];
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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;
 		}
 
-		((uint2*)state2)[0] ^= ((uint2*)state)[11];
+		ST4S(rowInOut, i, state2, thread, threads);
 
-		for (int j = 0; j < 11; j++)
-			((uint2*)state2)[j+1] ^= ((uint2*)state)[j];
+		LD4S(state1, rowOut, i, thread, threads);
 
+#pragma unroll
 		for (int j = 0; j < 3; j++)
-			(DMatrix + s2)[j] = state2[j];
+			state1[j] ^= state[j];
+
+		ST4S(rowOut, i, state1, thread, threads);
 	}
 }
 
 static __device__ __forceinline__
-void reduceDuplexRowt(const int rowIn, const int rowInOut, const int rowOut, uint2x4* state, const uint32_t thread)
+void reduceDuplexRowt_8(const int rowInOut, uint2* state, const uint32_t thread, const uint32_t threads)
 {
-	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);
+	uint2 state1[3], state2[3], last[3];
 
-	#pragma unroll 1
-	for (int i = 0; i < 8; i++)
-	{
-		uint2x4 state1[3], state2[3];
+	LD4S(state1, 2, 0, thread, threads);
+	LD4S(last, rowInOut, 0, thread, threads);
 
-		const uint32_t s1 = ps1 + i*memshift;
-		const uint32_t s2 = ps2 + i*memshift;
+	#pragma unroll
+	for (int j = 0; j < 3; j++)
+		state[j] ^= state1[j] + last[j];
 
-		for (int j = 0; j < 3; j++) {
-			state1[j] = __ldg4(&(DMatrix + s1)[j]);
-			state2[j] = __ldg4(&(DMatrix + s2)[j]);
-		}
+	round_lyra(state);
 
-		#pragma unroll
-		for (int j = 0; j < 3; j++) {
-			state1[j] += state2[j];
-			state[j]  ^= state1[j];
-		}
+	//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+	uint2 Data0 = state[0];
+	uint2 Data1 = state[1];
+	uint2 Data2 = state[2];
+	WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, threadIdx.x - 1, 4);
 
-		round_lyra(state);
+	if (threadIdx.x == 0)
+	{
+		last[0] ^= Data2;
+		last[1] ^= Data0;
+		last[2] ^= Data1;
+	} else {
+		last[0] ^= Data0;
+		last[1] ^= Data1;
+		last[2] ^= Data2;
+	}
 
-		((uint2*)state2)[0] ^= ((uint2*)state)[11];
+	if (rowInOut == 5)
+	{
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			last[j] ^= state[j];
+	}
 
-		for (int j = 0; j < 11; j++)
-			((uint2*)state2)[j + 1] ^= ((uint2*)state)[j];
+	for (int i = 1; i < Nrow; i++)
+	{
+		LD4S(state1, 2, i, thread, threads);
+		LD4S(state2, rowInOut, i, thread, threads);
 
-		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];
-			}
-		}
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
 	}
+
+	#pragma unroll
+	for (int j = 0; j < 3; j++)
+		state[j] ^= last[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)
+__constant__ uint2x4 blake2b_IV[2] = {
+	0xf3bcc908lu, 0x6a09e667lu,
+	0x84caa73blu, 0xbb67ae85lu,
+	0xfe94f82blu, 0x3c6ef372lu,
+	0x5f1d36f1lu, 0xa54ff53alu,
+	0xade682d1lu, 0x510e527flu,
+	0x2b3e6c1flu, 0x9b05688clu,
+	0xfb41bd6blu, 0x1f83d9ablu,
+	0x137e2179lu, 0x5be0cd19lu
+};
+
+__global__ __launch_bounds__(64, 1)
+void lyra2_gpu_hash_32_1(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)
 	{
 		uint2x4 state[4];
 
-		((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]);
-
-		state[1] = state[0];
+		state[0].x = state[1].x = __ldg(&g_hash[thread + threads * 0]);
+		state[0].y = state[1].y = __ldg(&g_hash[thread + threads * 1]);
+		state[0].z = state[1].z = __ldg(&g_hash[thread + threads * 2]);
+		state[0].w = state[1].w = __ldg(&g_hash[thread + threads * 3]);
 		state[2] = blake2b_IV[0];
 		state[3] = blake2b_IV[1];
 
 		for (int i = 0; i<24; i++)
 			round_lyra(state); //because 12 is not enough
 
-		const uint32_t ps1 = (memshift * 7  + 256 * thread);
-		for (int i = 0; i < 8; i++)
-		{
-			const uint32_t s1 = ps1 - memshift * i;
-			for (int j = 0; j < 3; j++)
-				(DMatrix + s1)[j] = (state)[j];
-			round_lyra(state);
-		}
+		((uint2x4*)DMatrix)[threads * 0 + thread] = state[0];
+		((uint2x4*)DMatrix)[threads * 1 + thread] = state[1];
+		((uint2x4*)DMatrix)[threads * 2 + thread] = state[2];
+		((uint2x4*)DMatrix)[threads * 3 + thread] = state[3];
+	}
+}
 
-		reduceDuplex(state, thread);
-
-		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 = 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);
-
-		const int32_t shift = (memshift * 8 * rowa + 256 * thread);
+__global__
+__launch_bounds__(TPB52, 1)
+void lyra2_gpu_hash_32_2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash)
+{
+	const uint32_t thread = blockDim.y * blockIdx.x + threadIdx.y;
 
-		#pragma unroll
-		for (int j = 0; j < 3; j++)
-			state[j] ^= __ldg4(&(DMatrix + shift)[j]);
+	if (thread < threads)
+	{
+		uint2 state[4];
+		state[0] = __ldg(&DMatrix[(0 * threads + thread) * blockDim.x + threadIdx.x]);
+		state[1] = __ldg(&DMatrix[(1 * threads + thread) * blockDim.x + threadIdx.x]);
+		state[2] = __ldg(&DMatrix[(2 * threads + thread) * blockDim.x + threadIdx.x]);
+		state[3] = __ldg(&DMatrix[(3 * threads + thread) * blockDim.x + threadIdx.x]);
+
+		reduceDuplex(state, thread, threads);
+		reduceDuplexRowSetup(1, 0, 2, state, thread, threads);
+		reduceDuplexRowSetup(2, 1, 3, state, thread, threads);
+		reduceDuplexRowSetup(3, 0, 4, state, thread, threads);
+		reduceDuplexRowSetup(4, 3, 5, state, thread, threads);
+		reduceDuplexRowSetup(5, 2, 6, state, thread, threads);
+		reduceDuplexRowSetup(6, 1, 7, state, thread, threads);
+
+		uint32_t rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(7, rowa, 0, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(0, rowa, 3, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(3, rowa, 6, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(6, rowa, 1, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(1, rowa, 4, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(4, rowa, 7, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt(7, rowa, 2, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowt_8(rowa, state, thread, threads);
+
+		DMatrix[(0 * threads + thread) * blockDim.x + threadIdx.x] = state[0];
+		DMatrix[(1 * threads + thread) * blockDim.x + threadIdx.x] = state[1];
+		DMatrix[(2 * threads + thread) * blockDim.x + threadIdx.x] = state[2];
+		DMatrix[(3 * threads + thread) * blockDim.x + threadIdx.x] = state[3];
+	}
+}
+
+__global__ __launch_bounds__(64, 1)
+void lyra2_gpu_hash_32_3(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
+{
+	const uint32_t thread = blockDim.x * blockIdx.x + threadIdx.x;
+
+	uint28 state[4];
+
+	if (thread < threads)
+	{
+		state[0] = __ldg4(&((uint2x4*)DMatrix)[threads * 0 + thread]);
+		state[1] = __ldg4(&((uint2x4*)DMatrix)[threads * 1 + thread]);
+		state[2] = __ldg4(&((uint2x4*)DMatrix)[threads * 2 + thread]);
+		state[3] = __ldg4(&((uint2x4*)DMatrix)[threads * 3 + 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];
-	}
+		g_hash[thread + threads * 0] = state[0].x;
+		g_hash[thread + threads * 1] = state[0].y;
+		g_hash[thread + threads * 2] = state[0].z;
+		g_hash[thread + threads * 3] = state[0].w;
+
+	} //thread
 }
 #else
+#if __CUDA_ARCH__ < 500
+
 /* for unsupported SM arch */
 __device__ void* DMatrix;
-__global__ void lyra2_gpu_hash_32(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
+#endif
+__global__ void lyra2_gpu_hash_32_1(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
+__global__ void lyra2_gpu_hash_32_2(uint32_t threads, uint32_t startNounce, uint64_t *g_hash) {}
+__global__ void lyra2_gpu_hash_32_3(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
 #endif
 
 __host__
-void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t* d_matrix)
+void lyra2_cpu_init(int thr_id, uint32_t threads, uint64_t *d_matrix)
 {
-	cuda_get_arch(thr_id);
+	// just assign the device pointer allocated in main loop
 	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)
+void lyra2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_hash, bool gtx750ti)
 {
 	int dev_id = device_map[thr_id % MAX_GPUS];
+
 	uint32_t tpb = TPB52;
-	if (device_sm[dev_id] == 500) tpb = TPB50;
-	if (device_sm[dev_id] == 350) tpb = TPB30; // to enhance (or not)
-	if (device_sm[dev_id] <= 300) tpb = TPB30;
 
-	dim3 grid((threads + tpb - 1) / tpb);
-	dim3 block(tpb);
+	if (cuda_arch[dev_id] >= 520) tpb = TPB52;
+	else if (cuda_arch[dev_id] >= 500) tpb = TPB50;
+	else if (cuda_arch[dev_id] >= 200) tpb = TPB20;
 
-	if (device_sm[dev_id] >= 500)
-		lyra2_gpu_hash_32 <<< grid, block >>> (threads, startNounce, (uint2*)d_hash);
-	else
-		lyra2_gpu_hash_32_sm2 <<< grid, block >>> (threads, startNounce, d_hash);
+	dim3 grid1((threads * 4 + tpb - 1) / tpb);
+	dim3 block1(4, tpb >> 2);
+
+	dim3 grid2((threads + 64 - 1) / 64);
+	dim3 block2(64);
 
+	dim3 grid3((threads + tpb - 1) / tpb);
+	dim3 block3(tpb);
+
+	if (cuda_arch[dev_id] >= 520)
+	{
+		lyra2_gpu_hash_32_1 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash);
+
+		lyra2_gpu_hash_32_2 <<< grid1, block1, 24 * (8 - 0) * sizeof(uint2) * tpb >>> (threads, startNounce, d_hash);
+
+		lyra2_gpu_hash_32_3 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash);
+	}
+	else if (cuda_arch[dev_id] >= 500)
+	{
+		size_t shared_mem = 0;
+
+		if (gtx750ti)
+			// 8Warpに調整のため、8192バイト確保する
+			shared_mem = 8192;
+		else
+			// 10Warpに調整のため、6144バイト確保する
+			shared_mem = 6144;
+
+		lyra2_gpu_hash_32_1_sm5 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash);
+
+		lyra2_gpu_hash_32_2_sm5 <<< grid1, block1, shared_mem >>> (threads, startNounce, (uint2*)d_hash);
+
+		lyra2_gpu_hash_32_3_sm5 <<< grid2, block2 >>> (threads, startNounce, (uint2*)d_hash);
+	}
+	else
+		lyra2_gpu_hash_32_sm2 <<< grid3, block3 >>> (threads, startNounce, d_hash);
 }

lyra2/cuda_lyra2_sm2.cuh

@@ -3,15 +3,16 @@
 #ifdef __INTELLISENSE__
 /* just for vstudio code colors */
 #undef __CUDA_ARCH__
-#define __CUDA_ARCH__ 300
+#define __CUDA_ARCH__ 500
 #endif
 
 #include "cuda_helper.h"
 
 #define TPB30 160
+#define TPB20 160
 
 #if (__CUDA_ARCH__ >= 200 && __CUDA_ARCH__ <= 350) || !defined(__CUDA_ARCH__)
-__constant__ static uint2 blake2b_IV[8] = {
+__constant__ static uint2 blake2b_IV_sm2[8] = {
 	{ 0xf3bcc908, 0x6a09e667 },
 	{ 0x84caa73b, 0xbb67ae85 },
 	{ 0xfe94f82b, 0x3c6ef372 },
@@ -149,7 +150,7 @@ void lyra2_gpu_hash_32_sm2(uint32_t threads, uint32_t startNounce, uint64_t *g_h
 
 		#pragma unroll
 		for (int i = 0; i<8; i++) {
-			state[i + 8] = blake2b_IV[i];
+			state[i + 8] = blake2b_IV_sm2[i];
 		}
 
 		// blake2blyra x2

lyra2/cuda_lyra2_sm5.cuh

@@ -0,0 +1,693 @@
+#include <memory.h>
+
+#ifdef __INTELLISENSE__
+/* just for vstudio code colors */
+#undef __CUDA_ARCH__
+#define __CUDA_ARCH__ 500
+#endif
+
+#include "cuda_helper.h"
+
+#define TPB50 32
+
+#if __CUDA_ARCH__ == 500
+#include "cuda_lyra2_vectors.h"
+
+#define Nrow 8
+#define Ncol 8
+#define memshift 3
+
+__device__ uint2 *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;
+}
+
+#if __CUDA_ARCH__ == 300
+__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
+{
+	return __shfl(a, b, c);
+}
+
+__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
+{
+	return make_uint2(__shfl(a.x, b, c), __shfl(a.y, b, c));
+}
+
+__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
+{
+	a1 = WarpShuffle(a1, b1, c);
+	a2 = WarpShuffle(a2, b2, c);
+	a3 = WarpShuffle(a3, b3, c);
+}
+
+#else
+__device__ __forceinline__ uint32_t WarpShuffle(uint32_t a, uint32_t b, uint32_t c)
+{
+	extern __shared__ uint2 shared_mem[];
+
+	const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
+	uint32_t *_ptr = (uint32_t*)shared_mem;
+
+	__threadfence_block();
+	uint32_t buf = _ptr[thread];
+
+	_ptr[thread] = a;
+	__threadfence_block();
+	uint32_t result = _ptr[(thread&~(c - 1)) + (b&(c - 1))];
+
+	__threadfence_block();
+	_ptr[thread] = buf;
+
+	__threadfence_block();
+	return result;
+}
+
+__device__ __forceinline__ uint2 WarpShuffle(uint2 a, uint32_t b, uint32_t c)
+{
+	extern __shared__ uint2 shared_mem[];
+
+	const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
+
+	__threadfence_block();
+	uint2 buf = shared_mem[thread];
+
+	shared_mem[thread] = a;
+	__threadfence_block();
+	uint2 result = shared_mem[(thread&~(c - 1)) + (b&(c - 1))];
+
+	__threadfence_block();
+	shared_mem[thread] = buf;
+
+	__threadfence_block();
+	return result;
+}
+
+__device__ __forceinline__ void WarpShuffle3(uint2 &a1, uint2 &a2, uint2 &a3, uint32_t b1, uint32_t b2, uint32_t b3, uint32_t c)
+{
+	extern __shared__ uint2 shared_mem[];
+
+	const uint32_t thread = blockDim.x * threadIdx.y + threadIdx.x;
+
+	__threadfence_block();
+	uint2 buf = shared_mem[thread];
+
+	shared_mem[thread] = a1;
+	__threadfence_block();
+	a1 = shared_mem[(thread&~(c - 1)) + (b1&(c - 1))];
+	__threadfence_block();
+	shared_mem[thread] = a2;
+	__threadfence_block();
+	a2 = shared_mem[(thread&~(c - 1)) + (b2&(c - 1))];
+	__threadfence_block();
+	shared_mem[thread] = a3;
+	__threadfence_block();
+	a3 = shared_mem[(thread&~(c - 1)) + (b3&(c - 1))];
+
+	__threadfence_block();
+	shared_mem[thread] = buf;
+	__threadfence_block();
+}
+
+#endif
+
+#if __CUDA_ARCH__ >= 300
+static __device__ __forceinline__
+void Gfunc(uint2 &a, uint2 &b, uint2 &c, uint2 &d)
+{
+	a += b; d ^= a; d = SWAPUINT2(d);
+	c += d; b ^= c; b = ROR24(b); //ROR2(b, 24);
+	a += b; d ^= a; d = ROR16(d);
+	c += d; b ^= c; b = ROR2(b, 63);
+}
+#endif
+
+__device__ __forceinline__ void round_lyra(uint2 s[4])
+{
+	Gfunc(s[0], s[1], s[2], s[3]);
+	WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 1, threadIdx.x + 2, threadIdx.x + 3, 4);
+	Gfunc(s[0], s[1], s[2], s[3]);
+	WarpShuffle3(s[1], s[2], s[3], threadIdx.x + 3, threadIdx.x + 2, threadIdx.x + 1, 4);
+}
+
+static __device__ __forceinline__
+void round_lyra(uint2x4* s)
+{
+	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);
+}
+
+static __device__ __forceinline__
+void reduceDuplexV5(uint2 state[4], const uint32_t thread, const uint32_t threads)
+{
+	uint2 state1[3], state2[3];
+
+	const uint32_t ps0 = (memshift * Ncol * 0 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps1 = (memshift * Ncol * 1 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps2 = (memshift * Ncol * 2 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps3 = (memshift * Ncol * 3 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps4 = (memshift * Ncol * 4 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps5 = (memshift * Ncol * 5 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps6 = (memshift * Ncol * 6 * threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps7 = (memshift * Ncol * 7 * threads + thread)*blockDim.x + threadIdx.x;
+
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s0 = memshift * Ncol * 0 + (Ncol - 1 - i) * memshift;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			ST4S(s0 + j, state[j]);
+		round_lyra(state);
+	}
+
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s0 = memshift * Ncol * 0 + i * memshift;
+		const uint32_t s1 = ps1 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = LD4S(s0 + j);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s1 + j*threads*blockDim.x) = state1[j] ^ state[j];
+	}
+
+	// 1, 0, 2
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s0 = memshift * Ncol * 0 + i * memshift;
+		const uint32_t s1 = ps1 + i * memshift* threads*blockDim.x;
+		const uint32_t s2 = ps2 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = *(DMatrix + s1 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state2[j] = LD4S(s0 + j);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s2 + j*threads*blockDim.x) = state1[j] ^ state[j];
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+			ST4S(s0 + j, state2[j]);
+	}
+
+	// 2, 1, 3
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s1 = ps1 + i * memshift* threads*blockDim.x;
+		const uint32_t s2 = ps2 + i * memshift* threads*blockDim.x;
+		const uint32_t s3 = ps3 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = *(DMatrix + s2 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state2[j] = *(DMatrix + s1 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s3 + j*threads*blockDim.x) = state1[j] ^ state[j];
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s1 + j*threads*blockDim.x) = state2[j];
+	}
+
+	// 3, 0, 4
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t ls0 = memshift * Ncol * 0 + i * memshift;
+		const uint32_t s0 = ps0 + i * memshift* threads*blockDim.x;
+		const uint32_t s3 = ps3 + i * memshift* threads*blockDim.x;
+		const uint32_t s4 = ps4 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = *(DMatrix + s3 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state2[j] = LD4S(ls0 + j);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s4 + j*threads*blockDim.x) = state1[j] ^ state[j];
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s0 + j*threads*blockDim.x) = state2[j];
+	}
+
+	// 4, 3, 5
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s3 = ps3 + i * memshift* threads*blockDim.x;
+		const uint32_t s4 = ps4 + i * memshift* threads*blockDim.x;
+		const uint32_t s5 = ps5 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = *(DMatrix + s4 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state2[j] = *(DMatrix + s3 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s5 + j*threads*blockDim.x) = state1[j] ^ state[j];
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s3 + j*threads*blockDim.x) = state2[j];
+	}
+
+	// 5, 2, 6
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s2 = ps2 + i * memshift* threads*blockDim.x;
+		const uint32_t s5 = ps5 + i * memshift* threads*blockDim.x;
+		const uint32_t s6 = ps6 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = *(DMatrix + s5 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state2[j] = *(DMatrix + s2 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s6 + j*threads*blockDim.x) = state1[j] ^ state[j];
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s2 + j*threads*blockDim.x) = state2[j];
+	}
+
+	// 6, 1, 7
+	for (int i = 0; i < 8; i++)
+	{
+		const uint32_t s1 = ps1 + i * memshift* threads*blockDim.x;
+		const uint32_t s6 = ps6 + i * memshift* threads*blockDim.x;
+		const uint32_t s7 = ps7 + (7 - i)*memshift* threads*blockDim.x;
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state1[j] = *(DMatrix + s6 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state2[j] = *(DMatrix + s1 + j*threads*blockDim.x);
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= state1[j] + state2[j];
+
+		round_lyra(state);
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s7 + j*threads*blockDim.x) = state1[j] ^ state[j];
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+			*(DMatrix + s1 + j*threads*blockDim.x) = state2[j];
+	}
+}
+
+static __device__ __forceinline__
+void reduceDuplexRowV50(const int rowIn, const int rowInOut, const int rowOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
+{
+	const uint32_t ps1 = (memshift * Ncol * rowIn*threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps2 = (memshift * Ncol * rowInOut *threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps3 = (memshift * Ncol * rowOut*threads + thread)*blockDim.x + threadIdx.x;
+
+	#pragma unroll 1
+	for (int i = 0; i < 8; i++)
+	{
+		uint2 state1[3], state2[3];
+
+		const uint32_t s1 = ps1 + i*memshift*threads *blockDim.x;
+		const uint32_t s2 = ps2 + i*memshift*threads *blockDim.x;
+		const uint32_t s3 = ps3 + i*memshift*threads *blockDim.x;
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++) {
+			state1[j] = *(DMatrix + s1 + j*threads*blockDim.x);
+			state2[j] = *(DMatrix + s2 + j*threads*blockDim.x);
+		}
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++) {
+			state1[j] += state2[j];
+			state[j] ^= state1[j];
+		}
+
+		round_lyra(state);
+
+		//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+		uint2 Data0 = state[0];
+		uint2 Data1 = state[1];
+		uint2 Data2 = state[2];
+		WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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
+		for (int j = 0; j < 3; j++)
+		{
+			*(DMatrix + s2 + j*threads*blockDim.x) = state2[j];
+			*(DMatrix + s3 + j*threads*blockDim.x) ^= state[j];
+		}
+	}
+}
+
+static __device__ __forceinline__
+void reduceDuplexRowV50_8(const int rowInOut, uint2 state[4], const uint32_t thread, const uint32_t threads)
+{
+	const uint32_t ps1 = (memshift * Ncol * 2*threads + thread)*blockDim.x + threadIdx.x;
+	const uint32_t ps2 = (memshift * Ncol * rowInOut *threads + thread)*blockDim.x + threadIdx.x;
+	// const uint32_t ps3 = (memshift * Ncol * 5*threads + thread)*blockDim.x + threadIdx.x;
+
+	uint2 state1[3], last[3];
+
+	#pragma unroll
+	for (int j = 0; j < 3; j++) {
+		state1[j] = *(DMatrix + ps1 + j*threads*blockDim.x);
+		last[j] = *(DMatrix + ps2 + j*threads*blockDim.x);
+	}
+
+	#pragma unroll
+	for (int j = 0; j < 3; j++) {
+		state1[j] += last[j];
+		state[j] ^= state1[j];
+	}
+
+	round_lyra(state);
+
+	//一個手前のスレッドからデータを貰う(同時に一個先のスレッドにデータを送る)
+	uint2 Data0 = state[0];
+	uint2 Data1 = state[1];
+	uint2 Data2 = state[2];
+	WarpShuffle3(Data0, Data1, Data2, threadIdx.x - 1, threadIdx.x - 1, 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 == 5)
+	{
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			last[j] ^= state[j];
+	}
+
+	for (int i = 1; i < 8; i++)
+	{
+		const uint32_t s1 = ps1 + i*memshift*threads *blockDim.x;
+		const uint32_t s2 = ps2 + i*memshift*threads *blockDim.x;
+
+		#pragma unroll
+		for (int j = 0; j < 3; j++)
+			state[j] ^= *(DMatrix + s1 + j*threads*blockDim.x) + *(DMatrix + s2 + j*threads*blockDim.x);
+
+		round_lyra(state);
+	}
+
+
+#pragma unroll
+	for (int j = 0; j < 3; j++)
+		state[j] ^= last[j];
+
+}
+
+__global__ __launch_bounds__(64, 1)
+void lyra2_gpu_hash_32_1_sm5(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)
+	{
+		uint2x4 state[4];
+
+		((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]);
+
+		state[1] = state[0];
+		state[2] = blake2b_IV[0];
+		state[3] = blake2b_IV[1];
+
+		for (int i = 0; i < 24; i++)
+			round_lyra(state); //because 12 is not enough
+
+		((uint2x4*)DMatrix)[0 * threads + thread] = state[0];
+		((uint2x4*)DMatrix)[1 * threads + thread] = state[1];
+		((uint2x4*)DMatrix)[2 * threads + thread] = state[2];
+		((uint2x4*)DMatrix)[3 * threads + thread] = state[3];
+	}
+}
+
+__global__ __launch_bounds__(TPB50, 1)
+void lyra2_gpu_hash_32_2_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
+{
+	const uint32_t thread = (blockDim.y * blockIdx.x + threadIdx.y);
+
+	if (thread < threads)
+	{
+		uint2 state[4];
+
+		state[0] = __ldg(&DMatrix[(0 * threads + thread)*blockDim.x + threadIdx.x]);
+		state[1] = __ldg(&DMatrix[(1 * threads + thread)*blockDim.x + threadIdx.x]);
+		state[2] = __ldg(&DMatrix[(2 * threads + thread)*blockDim.x + threadIdx.x]);
+		state[3] = __ldg(&DMatrix[(3 * threads + thread)*blockDim.x + threadIdx.x]);
+
+		reduceDuplexV5(state, thread, threads);
+
+		uint32_t rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(7, rowa, 0, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(0, rowa, 3, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(3, rowa, 6, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(6, rowa, 1, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(1, rowa, 4, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(4, rowa, 7, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50(7, rowa, 2, state, thread, threads);
+		rowa = WarpShuffle(state[0].x, 0, 4) & 7;
+		reduceDuplexRowV50_8(rowa, state, thread, threads);
+
+		DMatrix[(0 * threads + thread)*blockDim.x + threadIdx.x] = state[0];
+		DMatrix[(1 * threads + thread)*blockDim.x + threadIdx.x] = state[1];
+		DMatrix[(2 * threads + thread)*blockDim.x + threadIdx.x] = state[2];
+		DMatrix[(3 * threads + thread)*blockDim.x + threadIdx.x] = state[3];
+	}
+}
+
+__global__ __launch_bounds__(64, 1)
+void lyra2_gpu_hash_32_3_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash)
+{
+	const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
+
+	if (thread < threads)
+	{
+		uint2x4 state[4];
+
+		state[0] = __ldg4(&((uint2x4*)DMatrix)[0 * threads + thread]);
+		state[1] = __ldg4(&((uint2x4*)DMatrix)[1 * threads + thread]);
+		state[2] = __ldg4(&((uint2x4*)DMatrix)[2 * threads + thread]);
+		state[3] = __ldg4(&((uint2x4*)DMatrix)[3 * threads + 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];
+	}
+}
+
+#else
+/* if __CUDA_ARCH__ != 500 .. host */
+__global__ void lyra2_gpu_hash_32_1_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
+__global__ void lyra2_gpu_hash_32_2_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
+__global__ void lyra2_gpu_hash_32_3_sm5(uint32_t threads, uint32_t startNounce, uint2 *g_hash) {}
+#endif

lyra2/cuda_lyra2_vectors.h

@@ -13,7 +13,7 @@
 #include "cuda_helper.h"
 
 #if __CUDA_ARCH__ < 300
-#define __shfl(x, y) (x)
+#define __shfl(x, y, z) (x)
 #endif
 
 #if __CUDA_ARCH__ < 320 && !defined(__ldg4)

lyra2/lyra2RE.cu

@@ -23,7 +23,7 @@ extern void skein256_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNon
 extern void skein256_cpu_init(int thr_id, uint32_t threads);
 
 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 lyra2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNonce, uint64_t *d_outputHash, bool gtx750ti);
 
 extern void groestl256_cpu_init(int thr_id, uint32_t threads);
 extern void groestl256_cpu_free(int thr_id);
@@ -79,36 +79,55 @@ extern "C" void lyra2re_hash(void *state, const void *input)
 }
 
 static bool init[MAX_GPUS] = { 0 };
+static uint32_t throughput[MAX_GPUS] = { 0 };
 
 extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce, unsigned long *hashes_done)
 {
 	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()) ? 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] = 0x000f;
+		ptarget[7] = 0x00ff;
 
+	static __thread bool gtx750ti;
 	if (!init[thr_id])
 	{
-		cudaSetDevice(device_map[thr_id]);
+		int dev_id = device_map[thr_id];
+		cudaSetDevice(dev_id);
 		CUDA_LOG_ERROR();
 
-		blake256_cpu_init(thr_id, throughput);
-		keccak256_cpu_init(thr_id,throughput);
-		skein256_cpu_init(thr_id, throughput);
-		groestl256_cpu_init(thr_id, throughput);
+		int intensity = (device_sm[dev_id] >= 500 && !is_windows()) ? 17 : 16;
+		if (device_sm[device_map[thr_id]] == 500) intensity = 15;
+		int temp = intensity;
+		throughput[thr_id] = cuda_default_throughput(thr_id, 1U << intensity); // 18=256*256*4;
+		if (init[thr_id]) throughput[thr_id] = min(throughput[thr_id], max_nonce - first_nonce);
 
-		// 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]);
+		cudaDeviceProp props;
+		cudaGetDeviceProperties(&props, dev_id);
 
-		CUDA_SAFE_CALL(cudaMalloc(&d_hash[thr_id], (size_t)32 * throughput));
+		if (strstr(props.name, "750 Ti")) gtx750ti = true;
+		else gtx750ti = false;
+
+		blake256_cpu_init(thr_id, throughput[thr_id]);
+		keccak256_cpu_init(thr_id, throughput[thr_id]);
+		skein256_cpu_init(thr_id, throughput[thr_id]);
+		groestl256_cpu_init(thr_id, throughput[thr_id]);
+
+		if (device_sm[dev_id] >= 500)
+		{
+			size_t matrix_sz = device_sm[dev_id] > 500 ? sizeof(uint64_t) * 4 * 4 : sizeof(uint64_t) * 8 * 8 * 3 * 4;
+			CUDA_SAFE_CALL(cudaMalloc(&d_matrix[thr_id], matrix_sz * throughput[thr_id]));
+			lyra2_cpu_init(thr_id, throughput[thr_id], d_matrix[thr_id]);
+		}
+
+		CUDA_SAFE_CALL(cudaMalloc(&d_hash[thr_id], (size_t)32 * throughput[thr_id]));
 
 		init[thr_id] = true;
+		if (temp != intensity){
+			gpulog(LOG_INFO, thr_id, "Intensity set to %u, %u cuda threads",
+				intensity, throughput[thr_id]);
+		}
 	}
 
 	uint32_t _ALIGN(128) endiandata[20];
@@ -122,15 +141,15 @@ extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce,
 		int order = 0;
 		uint32_t foundNonce;
 
-		blake256_cpu_hash_80(thr_id, throughput, pdata[19], d_hash[thr_id], order++);
-		keccak256_cpu_hash_32(thr_id, throughput, pdata[19], d_hash[thr_id], order++);
-		lyra2_cpu_hash_32(thr_id, throughput, pdata[19], d_hash[thr_id], order++);
-		skein256_cpu_hash_32(thr_id, throughput, pdata[19], d_hash[thr_id], order++);
+		blake256_cpu_hash_80(thr_id, throughput[thr_id], pdata[19], d_hash[thr_id], order++);
+		keccak256_cpu_hash_32(thr_id, throughput[thr_id], pdata[19], d_hash[thr_id], order++);
+		lyra2_cpu_hash_32(thr_id, throughput[thr_id], pdata[19], d_hash[thr_id], gtx750ti);
+		skein256_cpu_hash_32(thr_id, throughput[thr_id], pdata[19], d_hash[thr_id], order++);
 		TRACE("S")
 
-		*hashes_done = pdata[19] - first_nonce + throughput;
+		*hashes_done = pdata[19] - first_nonce + throughput[thr_id];
 
-		foundNonce = groestl256_cpu_hash_32(thr_id, throughput, pdata[19], d_hash[thr_id], order++);
+		foundNonce = groestl256_cpu_hash_32(thr_id, throughput[thr_id], pdata[19], d_hash[thr_id], order++);
 		if (foundNonce != UINT32_MAX)
 		{
 			uint32_t _ALIGN(64) vhash64[8];
@@ -162,11 +181,11 @@ extern "C" int scanhash_lyra2(int thr_id, struct work* work, uint32_t max_nonce,
 			}
 		}
 
-		if ((uint64_t)throughput + pdata[19] >= max_nonce) {
+		if ((uint64_t)throughput[thr_id] + pdata[19] >= max_nonce) {
 			pdata[19] = max_nonce;
 			break;
 		}
-		pdata[19] += throughput;
+		pdata[19] += throughput[thr_id];
 
 	} while (!work_restart[thr_id].restart);