GOSTcoin support for ccminer CUDA miner project, compatible with most nvidia cards
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#include <cuda.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "sm_30_intrinsics.h"
#include <stdio.h>
#include <memory.h>
#include <stdint.h>
// aus cpu-miner.c
extern int device_map[8];
// diese Struktur wird in der Init Funktion angefordert
static cudaDeviceProp props[8];
static uint32_t *d_tempBranch1Nonces[8];
static uint32_t *d_numValid[8];
static uint32_t *h_numValid[8];
static uint32_t *d_partSum[2][8]; // für bis zu vier partielle Summen
// aus heavy.cu
extern cudaError_t MyStreamSynchronize(cudaStream_t stream, int situation, int thr_id);
// True/False tester
typedef uint32_t(*cuda_compactTestFunction_t)(uint32_t *inpHash);
__device__ uint32_t JackpotTrueTest(uint32_t *inpHash)
{
uint32_t tmp = inpHash[0] & 0x01;
return (tmp == 1);
}
__device__ uint32_t JackpotFalseTest(uint32_t *inpHash)
{
uint32_t tmp = inpHash[0] & 0x01;
return (tmp == 0);
}
__device__ cuda_compactTestFunction_t d_JackpotTrueFunction = JackpotTrueTest, d_JackpotFalseFunction = JackpotFalseTest;
cuda_compactTestFunction_t h_JackpotTrueFunction[8], h_JackpotFalseFunction[8];
// Setup-Funktionen
__host__ void jackpot_compactTest_cpu_init(int thr_id, int threads)
{
cudaGetDeviceProperties(&props[thr_id], device_map[thr_id]);
cudaMemcpyFromSymbol(&h_JackpotTrueFunction[thr_id], d_JackpotTrueFunction, sizeof(cuda_compactTestFunction_t));
cudaMemcpyFromSymbol(&h_JackpotFalseFunction[thr_id], d_JackpotFalseFunction, sizeof(cuda_compactTestFunction_t));
// wir brauchen auch Speicherplatz auf dem Device
cudaMalloc(&d_tempBranch1Nonces[thr_id], sizeof(uint32_t) * threads * 2);
cudaMalloc(&d_numValid[thr_id], 2*sizeof(uint32_t));
cudaMallocHost(&h_numValid[thr_id], 2*sizeof(uint32_t));
uint32_t s1;
s1 = (threads / 256) * 2;
cudaMalloc(&d_partSum[0][thr_id], sizeof(uint32_t) * s1); // BLOCKSIZE (Threads/Block)
cudaMalloc(&d_partSum[1][thr_id], sizeof(uint32_t) * s1); // BLOCKSIZE (Threads/Block)
}
#if __CUDA_ARCH__ < 300
/**
* __shfl_up() calculates a source lane ID by subtracting delta from the caller's lane ID, and clamping to the range 0..width-1
*/
#undef __shfl_up
#define __shfl_up(var, delta, width) (0)
#endif
// Die Summenfunktion (vom NVIDIA SDK)
__global__ void jackpot_compactTest_gpu_SCAN(uint32_t *data, int width, uint32_t *partial_sums=NULL, cuda_compactTestFunction_t testFunc=NULL, int threads=0, uint32_t startNounce=0, uint32_t *inpHashes=NULL, uint32_t *d_validNonceTable=NULL)
{
extern __shared__ uint32_t sums[];
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;
}
}
// Uniform add: add partial sums array
__global__ void jackpot_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;
}
// Der Scatter
__global__ void jackpot_compactTest_gpu_SCATTER(uint32_t *sum, uint32_t *outp, cuda_compactTestFunction_t testFunc, int 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 nounce = startNounce + id;
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 jackpot_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 jackpot_compactTest_cpu_singleCompaction(int thr_id, int 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 = (int)jackpot_compactTest_roundUpExp((uint32_t)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
jackpot_compactTest_gpu_SCAN<<<thr1,blockSize, 32*sizeof(uint32_t)>>>(
d_tempBranch1Nonces[thr_id], 32, d_partSum[0][thr_id], function, orgThreads, startNounce, inpHashes, d_validNonceTable);
// weitere Scans
if(callThrid)
{
jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 32*sizeof(uint32_t)>>>(d_partSum[0][thr_id], 32, d_partSum[1][thr_id]);
jackpot_compactTest_gpu_SCAN<<<1, thr2, 32*sizeof(uint32_t)>>>(d_partSum[1][thr_id], (thr2>32) ? 32 : thr2);
}else
{
jackpot_compactTest_gpu_SCAN<<<thr3,blockSize2, 32*sizeof(uint32_t)>>>(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);
// Addieren
if(callThrid)
{
jackpot_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum[0][thr_id]+blockSize, d_partSum[1][thr_id], blockSize*thr2);
}
jackpot_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch1Nonces[thr_id]+blockSize, d_partSum[0][thr_id], threads);
// Scatter
jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch1Nonces[thr_id], d_nonces1,
function, orgThreads, startNounce, inpHashes, d_validNonceTable);
// Sync
cudaStreamSynchronize(NULL);
}
////// ACHTUNG: Diese funktion geht aktuell nur mit threads > 65536 (Am besten 256 * 1024 oder 256*2048)
__host__ void jackpot_compactTest_cpu_dualCompaction(int thr_id, int threads, uint32_t *nrm,
uint32_t *d_nonces1, uint32_t *d_nonces2,
uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable)
{
jackpot_compactTest_cpu_singleCompaction(thr_id, threads, &nrm[0], d_nonces1, h_JackpotTrueFunction[thr_id], startNounce, inpHashes, d_validNonceTable);
jackpot_compactTest_cpu_singleCompaction(thr_id, threads, &nrm[1], d_nonces2, h_JackpotFalseFunction[thr_id], startNounce, inpHashes, d_validNonceTable);
/*
// threadsPerBlock ausrechnen
int blockSize = 256;
int thr1 = threads / blockSize;
int thr2 = threads / (blockSize*blockSize);
// 1
jackpot_compactTest_gpu_SCAN<<<thr1,blockSize, 32*sizeof(uint32_t)>>>(d_tempBranch1Nonces[thr_id], 32, d_partSum1[thr_id], h_JackpotTrueFunction[thr_id], threads, startNounce, inpHashes);
jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 32*sizeof(uint32_t)>>>(d_partSum1[thr_id], 32, d_partSum2[thr_id]);
jackpot_compactTest_gpu_SCAN<<<1, thr2, 32*sizeof(uint32_t)>>>(d_partSum2[thr_id], (thr2>32) ? 32 : thr2);
cudaStreamSynchronize(NULL);
cudaMemcpy(&nrm[0], &(d_partSum2[thr_id])[thr2-1], sizeof(uint32_t), cudaMemcpyDeviceToHost);
jackpot_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum1[thr_id]+blockSize, d_partSum2[thr_id], blockSize*thr2);
jackpot_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch1Nonces[thr_id]+blockSize, d_partSum1[thr_id], threads);
// 2
jackpot_compactTest_gpu_SCAN<<<thr1,blockSize, 32*sizeof(uint32_t)>>>(d_tempBranch2Nonces[thr_id], 32, d_partSum1[thr_id], h_JackpotFalseFunction[thr_id], threads, startNounce, inpHashes);
jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 32*sizeof(uint32_t)>>>(d_partSum1[thr_id], 32, d_partSum2[thr_id]);
jackpot_compactTest_gpu_SCAN<<<1, thr2, 32*sizeof(uint32_t)>>>(d_partSum2[thr_id], (thr2>32) ? 32 : thr2);
cudaStreamSynchronize(NULL);
cudaMemcpy(&nrm[1], &(d_partSum2[thr_id])[thr2-1], sizeof(uint32_t), cudaMemcpyDeviceToHost);
jackpot_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum1[thr_id]+blockSize, d_partSum2[thr_id], blockSize*thr2);
jackpot_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch2Nonces[thr_id]+blockSize, d_partSum1[thr_id], threads);
// Hier ist noch eine Besonderheit: in d_tempBranch1Nonces sind die element von 1...nrm1 die Interessanten
// Schritt 3: Scatter
jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch1Nonces[thr_id], d_nonces1, h_JackpotTrueFunction[thr_id], threads, startNounce, inpHashes);
jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch2Nonces[thr_id], d_nonces2, h_JackpotFalseFunction[thr_id], threads, startNounce, inpHashes);
cudaStreamSynchronize(NULL);
*/
}
__host__ void jackpot_compactTest_cpu_hash_64(int thr_id, int threads, uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable,
uint32_t *d_nonces1, size_t *nrm1,
uint32_t *d_nonces2, size_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!
jackpot_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 = (size_t)h_numValid[thr_id][0];
*nrm2 = (size_t)h_numValid[thr_id][1];
}