ccminer/lyra2/cuda_lyra2v2.cu

#include <stdio.h>
#include <memory.h>

#ifdef __INTELLISENSE__
#define __CUDA_ARCH__ 500
#endif

#include "cuda_lyra2_vectors.h"

#define TPB 16

#define Nrow 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 vectype uint28
#define memshift 3
#else
#define u64type uint2
#define vectype uint28
#define memshift 3
#endif

__device__ vectype *DMatrix;

#if __CUDA_ARCH__ >= 320

#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);
	c += d; b ^= c; b = ROR24(b);
	a += b; d ^= a; d = ROR16(d);
	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__
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 reduceDuplex(vectype state[4], uint32_t thread)
{
	vectype state1[3];
	uint32_t ps1 = (Nrow * Ncol * memshift * thread);
	uint32_t ps2 = (memshift * (Ncol-1) + memshift * Ncol + Nrow * Ncol * memshift * thread);

	#pragma unroll 4
	for (int i = 0; i < Ncol; i++)
	{
		uint32_t s1 = ps1 + i*memshift;
		uint32_t s2 = ps2 - i*memshift;

		for (int j = 0; j < 3; j++)
			state1[j] = __ldg4(&(DMatrix+s1)[j]);

		for (int j = 0; j < 3; j++)
			state[j] ^= state1[j];
		round_lyra_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 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 reduceDuplexRowSetupV2(const int rowIn, const int rowInOut, const int rowOut, vectype state[4], uint32_t thread)
{
	vectype state2[3],state1[3];

	uint32_t ps1 = (memshift * Ncol * rowIn + 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);

	//#pragma unroll 1
	for (int i = 0; i < Ncol; i++)
	{
		uint32_t s1 = ps1 + i*memshift;
		uint32_t s2 = ps2 + i*memshift;
		uint32_t s3 = ps3 - 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 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 reduceDuplexRowtV2(const int rowIn, const int rowInOut, const int rowOut, vectype* state, uint32_t thread)
{
	vectype state1[3],state2[3];
	uint32_t ps1 = (memshift * Ncol * rowIn    + Nrow * Ncol * memshift * thread);
	uint32_t ps2 = (memshift * Ncol * rowInOut + Nrow * Ncol * memshift * thread);
	uint32_t ps3 = (memshift * Ncol * rowOut   + Nrow * Ncol * memshift * thread);

	//#pragma unroll 1
	for (int i = 0; i < Ncol; i++)
	{
		uint32_t s1 = ps1 + i*memshift;
		uint32_t s2 = ps2 + i*memshift;
		uint32_t s3 = ps3 + 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];
		}

	}
}

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];
		}
	}
}


#if __CUDA_ARCH__ < 500
__global__	__launch_bounds__(128, 1)
#elif __CUDA_ARCH__ == 500
__global__	__launch_bounds__(16, 1)
#else
__global__	__launch_bounds__(TPB, 1)
#endif
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 __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 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 = (memshift * (Ncol - 1) + Nrow * Ncol * memshift * thread);

		for (int i = 0; i < Ncol; i++)
		{
			uint32_t s1 = ps1 - memshift * i;
			for (int j = 0; j < 3; j++)
				(DMatrix + s1)[j] = (state)[j];

			round_lyra_v35(state);
		}

		reduceDuplex(state, thread);

		reduceDuplexRowSetupV2(1, 0, 2, state,  thread);
		reduceDuplexRowSetupV2(2, 1, 3, state,  thread);

		uint32_t rowa;
		int prev=3;

		for (int i = 0; i < 4; i++) {
			rowa = ((uint2*)state)[0].x & 3;
			reduceDuplexRowtV2(prev, rowa, i, state, thread);
			prev=i;
		}

		uint32_t shift = (memshift * Ncol * rowa + Nrow * Ncol * 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
}
#else /*__CUDA_ARCH__ >= 320 */
__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

__host__
void lyra2v2_cpu_init(int thr_id, uint32_t threads,uint64_t *hash)
{
	cudaMemcpyToSymbol(DMatrix, &hash, sizeof(hash), 0, cudaMemcpyHostToDevice);
}

__host__
void lyra2v2_cpu_hash_32(int thr_id, uint32_t threads, uint32_t startNounce, uint64_t *d_outputHash, int order)
{
	uint32_t tpb;
	if (device_sm[device_map[thr_id]] < 500)
		tpb = 64;
	else if (device_sm[device_map[thr_id]] == 500)
		tpb = 32;
	else
		tpb = TPB;

	dim3 grid((threads + tpb - 1) / tpb);
	dim3 block(tpb);

	if (device_sm[device_map[thr_id]] >= 500)
		lyra2v2_gpu_hash_32 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash);
	else
		lyra2v2_gpu_hash_32_v3 <<<grid, block>>> (threads, startNounce, (uint2*)d_outputHash);

	MyStreamSynchronize(NULL, order, thr_id);
}