GOSTcoin support for ccminer CUDA miner project, compatible with most nvidia cards
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/**
* __shfl_up require SM 3.0 arch!
*
* SM 2 alternative method by tpruvot@github 2015
*/
#include <stdio.h>
#include <memory.h>
#include "cuda_helper.h"
#ifdef __INTELLISENSE__
/* just for vstudio code colors */
#define __CUDA_ARCH__ 300
#define __shfl_up(var, delta, width) (0)
#endif
static uint32_t *h_numValid[MAX_GPUS];
static uint32_t *d_tempBranch1Nonces[MAX_GPUS];
static uint32_t *d_partSum[2][MAX_GPUS]; // für bis zu vier partielle Summen
// True/False tester
typedef uint32_t(*cuda_compactTestFunction_t)(uint32_t *inpHash);
__device__ uint32_t QuarkTrueTest(uint32_t *inpHash)
{
return ((inpHash[0] & 0x08) == 0x08);
}
__device__ uint32_t QuarkFalseTest(uint32_t *inpHash)
{
return ((inpHash[0] & 0x08) == 0);
}
__device__ cuda_compactTestFunction_t d_QuarkTrueFunction = QuarkTrueTest, d_QuarkFalseFunction = QuarkFalseTest;
cuda_compactTestFunction_t h_QuarkTrueFunction[MAX_GPUS], h_QuarkFalseFunction[MAX_GPUS];
// Setup/Alloc Function
__host__
void quark_compactTest_cpu_init(int thr_id, uint32_t threads)
{
int dev_id = device_map[thr_id];
cuda_get_arch(thr_id);
cudaMemcpyFromSymbol(&h_QuarkTrueFunction[thr_id], d_QuarkTrueFunction, sizeof(cuda_compactTestFunction_t));
cudaMemcpyFromSymbol(&h_QuarkFalseFunction[thr_id], d_QuarkFalseFunction, sizeof(cuda_compactTestFunction_t));
if (cuda_arch[dev_id] >= 300) {
uint32_t s1 = (threads / 256) * 2;
CUDA_SAFE_CALL(cudaMalloc(&d_tempBranch1Nonces[thr_id], sizeof(uint32_t) * threads * 2));
CUDA_SAFE_CALL(cudaMalloc(&d_partSum[0][thr_id], sizeof(uint32_t) * s1)); // BLOCKSIZE (Threads/Block)
CUDA_SAFE_CALL(cudaMalloc(&d_partSum[1][thr_id], sizeof(uint32_t) * s1)); // BLOCKSIZE (Threads/Block)
} else {
CUDA_SAFE_CALL(cudaMalloc(&d_tempBranch1Nonces[thr_id], sizeof(uint32_t) * threads));
}
cudaMallocHost(&h_numValid[thr_id], 2*sizeof(uint32_t));
}
// Because all alloc should have a free...
__host__
void quark_compactTest_cpu_free(int thr_id)
{
int dev_id = device_map[thr_id];
cudaFreeHost(h_numValid[thr_id]);
if (cuda_arch[dev_id] >= 300) {
cudaFree(d_tempBranch1Nonces[thr_id]);
cudaFree(d_partSum[0][thr_id]);
cudaFree(d_partSum[1][thr_id]);
} else {
cudaFree(d_tempBranch1Nonces[thr_id]);
}
}
__global__
void quark_compactTest_gpu_SCAN(uint32_t *data, const int width, uint32_t *partial_sums=NULL, cuda_compactTestFunction_t testFunc=NULL,
uint32_t threads=0, uint32_t startNounce=0, uint32_t *inpHashes=NULL, uint32_t *d_validNonceTable=NULL)
{
#if __CUDA_ARCH__ >= 300
__shared__ uint32_t sums[32];
int id = ((blockIdx.x * blockDim.x) + threadIdx.x);
//int lane_id = id % warpSize;
int lane_id = id % width;
// determine a warp_id within a block
//int warp_id = threadIdx.x / warpSize;
int warp_id = threadIdx.x / width;
sums[lane_id] = 0;
// Below is the basic structure of using a shfl instruction
// for a scan.
// Record "value" as a variable - we accumulate it along the way
uint32_t value;
if(testFunc != NULL)
{
if (id < threads)
{
uint32_t *inpHash;
if(d_validNonceTable == NULL)
{
// keine Nonce-Liste
inpHash = &inpHashes[id<<4];
} else {
// Nonce-Liste verfügbar
int nonce = d_validNonceTable[id] - startNounce;
inpHash = &inpHashes[nonce<<4];
}
value = (*testFunc)(inpHash);
} else {
value = 0;
}
} else {
value = data[id];
}
__syncthreads();
// Now accumulate in log steps up the chain
// compute sums, with another thread's value who is
// distance delta away (i). Note
// those threads where the thread 'i' away would have
// been out of bounds of the warp are unaffected. This
// creates the scan sum.
#pragma unroll
for (int i=1; i<=width; i*=2)
{
uint32_t n = __shfl_up((int)value, i, width);
if (lane_id >= i) value += n;
}
// value now holds the scan value for the individual thread
// next sum the largest values for each warp
// write the sum of the warp to smem
//if (threadIdx.x % warpSize == warpSize-1)
if (threadIdx.x % width == width-1)
{
sums[warp_id] = value;
}
__syncthreads();
//
// scan sum the warp sums
// the same shfl scan operation, but performed on warp sums
//
if (warp_id == 0)
{
uint32_t warp_sum = sums[lane_id];
for (int i=1; i<=width; i*=2)
{
uint32_t n = __shfl_up((int)warp_sum, i, width);
if (lane_id >= i) warp_sum += n;
}
sums[lane_id] = warp_sum;
}
__syncthreads();
// perform a uniform add across warps in the block
// read neighbouring warp's sum and add it to threads value
uint32_t blockSum = 0;
if (warp_id > 0)
{
blockSum = sums[warp_id-1];
}
value += blockSum;
// Now write out our result
data[id] = value;
// last thread has sum, write write out the block's sum
if (partial_sums != NULL && threadIdx.x == blockDim.x-1)
{
partial_sums[blockIdx.x] = value;
}
#endif // SM3+
}
// Uniform add: add partial sums array
__global__
void quark_compactTest_gpu_ADD(uint32_t *data, uint32_t *partial_sums, int len)
{
__shared__ uint32_t buf;
int id = ((blockIdx.x * blockDim.x) + threadIdx.x);
if (id > len) return;
if (threadIdx.x == 0)
{
buf = partial_sums[blockIdx.x];
}
__syncthreads();
data[id] += buf;
}
__global__
void quark_compactTest_gpu_SCATTER(uint32_t *sum, uint32_t *outp, cuda_compactTestFunction_t testFunc,
uint32_t threads=0, uint32_t startNounce=0, uint32_t *inpHashes=NULL, uint32_t *d_validNonceTable=NULL)
{
int id = ((blockIdx.x * blockDim.x) + threadIdx.x);
uint32_t actNounce = id;
uint32_t value;
if (id < threads)
{
uint32_t *inpHash;
if(d_validNonceTable == NULL)
{
// keine Nonce-Liste
inpHash = &inpHashes[id<<4];
} else {
// Nonce-Liste verfügbar
int nonce = d_validNonceTable[id] - startNounce;
actNounce = nonce;
inpHash = &inpHashes[nonce<<4];
}
value = (*testFunc)(inpHash);
} else {
value = 0;
}
if (value) {
int idx = sum[id];
if(idx > 0)
outp[idx-1] = startNounce + actNounce;
}
}
__host__ static uint32_t quark_compactTest_roundUpExp(uint32_t val)
{
if(val == 0)
return 0;
uint32_t mask = 0x80000000;
while( (val & mask) == 0 ) mask = mask >> 1;
if( (val & (~mask)) != 0 )
return mask << 1;
return mask;
}
__host__
void quark_compactTest_cpu_singleCompaction(int thr_id, uint32_t threads, uint32_t *nrm,uint32_t *d_nonces1,
cuda_compactTestFunction_t function, uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable)
{
int orgThreads = threads;
threads = quark_compactTest_roundUpExp(threads);
// threadsPerBlock ausrechnen
int blockSize = 256;
int nSummen = threads / blockSize;
int thr1 = (threads+blockSize-1) / blockSize;
int thr2 = threads / (blockSize*blockSize);
int blockSize2 = (nSummen < blockSize) ? nSummen : blockSize;
int thr3 = (nSummen + blockSize2-1) / blockSize2;
bool callThrid = (thr2 > 0) ? true : false;
// Erster Initialscan
quark_compactTest_gpu_SCAN <<<thr1,blockSize>>>(
d_tempBranch1Nonces[thr_id], 32, d_partSum[0][thr_id], function, orgThreads, startNounce, inpHashes, d_validNonceTable);
// weitere Scans
if(callThrid) {
quark_compactTest_gpu_SCAN<<<thr2,blockSize>>>(d_partSum[0][thr_id], 32, d_partSum[1][thr_id]);
quark_compactTest_gpu_SCAN<<<1, thr2>>>(d_partSum[1][thr_id], (thr2>32) ? 32 : thr2);
} else {
quark_compactTest_gpu_SCAN<<<thr3,blockSize2>>>(d_partSum[0][thr_id], (blockSize2>32) ? 32 : blockSize2);
}
// Sync + Anzahl merken
cudaStreamSynchronize(NULL);
if(callThrid)
cudaMemcpy(nrm, &(d_partSum[1][thr_id])[thr2-1], sizeof(uint32_t), cudaMemcpyDeviceToHost);
else
cudaMemcpy(nrm, &(d_partSum[0][thr_id])[nSummen-1], sizeof(uint32_t), cudaMemcpyDeviceToHost);
if(callThrid) {
quark_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum[0][thr_id]+blockSize, d_partSum[1][thr_id], blockSize*thr2);
}
quark_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch1Nonces[thr_id]+blockSize, d_partSum[0][thr_id], threads);
quark_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch1Nonces[thr_id], d_nonces1,
function, orgThreads, startNounce, inpHashes, d_validNonceTable);
// Sync
cudaStreamSynchronize(NULL);
}
#if __CUDA_ARCH__ < 300
__global__ __launch_bounds__(128, 8)
void quark_filter_gpu_sm2(const uint32_t threads, const uint32_t* d_hash, uint32_t* d_branch2, uint32_t* d_NonceBranch)
{
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads)
{
const uint32_t offset = thread * 16U; // 64U / sizeof(uint32_t);
uint4 *psrc = (uint4*) (&d_hash[offset]);
d_NonceBranch[thread] = ((uint8_t*)psrc)[0] & 0x8;
if (d_NonceBranch[thread]) return;
// uint4 = 4x uint32_t = 16 bytes
uint4 *pdst = (uint4*) (&d_branch2[offset]);
pdst[0] = psrc[0];
pdst[1] = psrc[1];
pdst[2] = psrc[2];
pdst[3] = psrc[3];
}
}
__global__ __launch_bounds__(128, 8)
void quark_merge_gpu_sm2(const uint32_t threads, uint32_t* d_hash, uint32_t* d_branch2, uint32_t* const d_NonceBranch)
{
const uint32_t thread = (blockDim.x * blockIdx.x + threadIdx.x);
if (thread < threads && !d_NonceBranch[thread])
{
const uint32_t offset = thread * 16U;
uint4 *pdst = (uint4*) (&d_hash[offset]);
uint4 *psrc = (uint4*) (&d_branch2[offset]);
pdst[0] = psrc[0];
pdst[1] = psrc[1];
pdst[2] = psrc[2];
pdst[3] = psrc[3];
}
}
#else
__global__ void quark_filter_gpu_sm2(const uint32_t threads, const uint32_t* d_hash, uint32_t* d_branch2, uint32_t* d_NonceBranch) {}
__global__ void quark_merge_gpu_sm2(const uint32_t threads, uint32_t* d_hash, uint32_t* d_branch2, uint32_t* const d_NonceBranch) {}
#endif
__host__
uint32_t quark_filter_cpu_sm2(const int thr_id, const uint32_t threads, const uint32_t *inpHashes, uint32_t* d_branch2)
{
const uint32_t threadsperblock = 128;
dim3 grid((threads + threadsperblock - 1) / threadsperblock);
dim3 block(threadsperblock);
// extract algo permution hashes to a second branch buffer
quark_filter_gpu_sm2 <<<grid, block>>> (threads, inpHashes, d_branch2, d_tempBranch1Nonces[thr_id]);
return threads;
}
__host__
void quark_merge_cpu_sm2(const int thr_id, const uint32_t threads, uint32_t *outpHashes, uint32_t* d_branch2)
{
const uint32_t threadsperblock = 128;
dim3 grid((threads + threadsperblock - 1) / threadsperblock);
dim3 block(threadsperblock);
// put back second branch hashes to the common buffer d_hash
quark_merge_gpu_sm2 <<<grid, block>>> (threads, outpHashes, d_branch2, d_tempBranch1Nonces[thr_id]);
}
////// ACHTUNG: Diese funktion geht aktuell nur mit threads > 65536 (Am besten 256 * 1024 oder 256*2048)
__host__
void quark_compactTest_cpu_dualCompaction(int thr_id, uint32_t threads, uint32_t *nrm, uint32_t *d_nonces1,
uint32_t *d_nonces2, uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable)
{
quark_compactTest_cpu_singleCompaction(thr_id, threads, &nrm[0], d_nonces1, h_QuarkTrueFunction[thr_id], startNounce, inpHashes, d_validNonceTable);
quark_compactTest_cpu_singleCompaction(thr_id, threads, &nrm[1], d_nonces2, h_QuarkFalseFunction[thr_id], startNounce, inpHashes, d_validNonceTable);
}
__host__
void quark_compactTest_cpu_hash_64(int thr_id, uint32_t threads, uint32_t startNounce, uint32_t *inpHashes,
uint32_t *d_validNonceTable, uint32_t *d_nonces1, uint32_t *nrm1, uint32_t *d_nonces2, uint32_t *nrm2, int order)
{
// Wenn validNonceTable genutzt wird, dann werden auch nur die Nonces betrachtet, die dort enthalten sind
// "threads" ist in diesem Fall auf die Länge dieses Array's zu setzen!
quark_compactTest_cpu_dualCompaction(thr_id, threads,
h_numValid[thr_id], d_nonces1, d_nonces2,
startNounce, inpHashes, d_validNonceTable);
cudaStreamSynchronize(NULL); // Das original braucht zwar etwas CPU-Last, ist an dieser Stelle aber evtl besser
*nrm1 = h_numValid[thr_id][0];
*nrm2 = h_numValid[thr_id][1];
}
__host__
void quark_compactTest_single_false_cpu_hash_64(int thr_id, uint32_t threads, uint32_t startNounce, uint32_t *inpHashes,
uint32_t *d_validNonceTable, uint32_t *d_nonces1, uint32_t *nrm1, int order)
{
// Wenn validNonceTable genutzt wird, dann werden auch nur die Nonces betrachtet, die dort enthalten sind
// "threads" ist in diesem Fall auf die Länge dieses Array's zu setzen!
quark_compactTest_cpu_singleCompaction(thr_id, threads, h_numValid[thr_id], d_nonces1, h_QuarkFalseFunction[thr_id], startNounce, inpHashes, d_validNonceTable);
cudaStreamSynchronize(NULL);
*nrm1 = h_numValid[thr_id][0];
}