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347 lines
11 KiB
347 lines
11 KiB
#include <stdio.h> |
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#include <memory.h> |
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#include "cuda_helper.h" |
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#include <sm_30_intrinsics.h> |
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static uint32_t *d_tempBranch1Nonces[MAX_GPUS]; |
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static uint32_t *d_numValid[MAX_GPUS]; |
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static uint32_t *h_numValid[MAX_GPUS]; |
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static uint32_t *d_partSum[2][MAX_GPUS]; // für bis zu vier partielle Summen |
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// True/False tester |
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typedef uint32_t(*cuda_compactTestFunction_t)(uint32_t *inpHash); |
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__device__ uint32_t JackpotTrueTest(uint32_t *inpHash) |
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{ |
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uint32_t tmp = inpHash[0] & 0x01; |
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return (tmp == 1); |
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} |
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__device__ uint32_t JackpotFalseTest(uint32_t *inpHash) |
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{ |
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uint32_t tmp = inpHash[0] & 0x01; |
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return (tmp == 0); |
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} |
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__device__ cuda_compactTestFunction_t d_JackpotTrueFunction = JackpotTrueTest, d_JackpotFalseFunction = JackpotFalseTest; |
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cuda_compactTestFunction_t h_JackpotTrueFunction[MAX_GPUS], h_JackpotFalseFunction[MAX_GPUS]; |
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// Setup-Funktionen |
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__host__ void jackpot_compactTest_cpu_init(int thr_id, int threads) |
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{ |
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cudaMemcpyFromSymbol(&h_JackpotTrueFunction[thr_id], d_JackpotTrueFunction, sizeof(cuda_compactTestFunction_t)); |
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cudaMemcpyFromSymbol(&h_JackpotFalseFunction[thr_id], d_JackpotFalseFunction, sizeof(cuda_compactTestFunction_t)); |
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// wir brauchen auch Speicherplatz auf dem Device |
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cudaMalloc(&d_tempBranch1Nonces[thr_id], sizeof(uint32_t) * threads * 2); |
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cudaMalloc(&d_numValid[thr_id], 2*sizeof(uint32_t)); |
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cudaMallocHost(&h_numValid[thr_id], 2*sizeof(uint32_t)); |
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uint32_t s1; |
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s1 = (threads / 256) * 2; |
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cudaMalloc(&d_partSum[0][thr_id], sizeof(uint32_t) * s1); // BLOCKSIZE (Threads/Block) |
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cudaMalloc(&d_partSum[1][thr_id], sizeof(uint32_t) * s1); // BLOCKSIZE (Threads/Block) |
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} |
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#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 300 |
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/** |
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* __shfl_up() calculates a source lane ID by subtracting delta from the caller's lane ID, and clamping to the range 0..width-1 |
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*/ |
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#undef __shfl_up |
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#define __shfl_up(var, delta, width) (0) |
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#endif |
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// Die Summenfunktion (vom NVIDIA SDK) |
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__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) |
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{ |
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extern __shared__ uint32_t sums[]; |
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int id = ((blockIdx.x * blockDim.x) + threadIdx.x); |
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//int lane_id = id % warpSize; |
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int lane_id = id % width; |
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// determine a warp_id within a block |
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//int warp_id = threadIdx.x / warpSize; |
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int warp_id = threadIdx.x / width; |
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sums[lane_id] = 0; |
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// Below is the basic structure of using a shfl instruction |
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// for a scan. |
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// Record "value" as a variable - we accumulate it along the way |
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uint32_t value; |
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if(testFunc != NULL) |
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{ |
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if (id < threads) |
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{ |
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uint32_t *inpHash; |
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if(d_validNonceTable == NULL) |
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{ |
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// keine Nonce-Liste |
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inpHash = &inpHashes[id<<4]; |
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}else |
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{ |
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// Nonce-Liste verfügbar |
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int nonce = d_validNonceTable[id] - startNounce; |
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inpHash = &inpHashes[nonce<<4]; |
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} |
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value = (*testFunc)(inpHash); |
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}else |
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{ |
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value = 0; |
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} |
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}else |
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{ |
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value = data[id]; |
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} |
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__syncthreads(); |
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// Now accumulate in log steps up the chain |
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// compute sums, with another thread's value who is |
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// distance delta away (i). Note |
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// those threads where the thread 'i' away would have |
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// been out of bounds of the warp are unaffected. This |
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// creates the scan sum. |
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#pragma unroll |
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for (int i=1; i<=width; i*=2) |
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{ |
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uint32_t n = __shfl_up((int)value, i, width); |
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if (lane_id >= i) value += n; |
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} |
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// value now holds the scan value for the individual thread |
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// next sum the largest values for each warp |
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// write the sum of the warp to smem |
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//if (threadIdx.x % warpSize == warpSize-1) |
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if (threadIdx.x % width == width-1) |
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{ |
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sums[warp_id] = value; |
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} |
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__syncthreads(); |
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// |
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// scan sum the warp sums |
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// the same shfl scan operation, but performed on warp sums |
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// |
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if (warp_id == 0) |
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{ |
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uint32_t warp_sum = sums[lane_id]; |
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for (int i=1; i<=width; i*=2) |
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{ |
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uint32_t n = __shfl_up((int)warp_sum, i, width); |
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if (lane_id >= i) warp_sum += n; |
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} |
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sums[lane_id] = warp_sum; |
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} |
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__syncthreads(); |
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// perform a uniform add across warps in the block |
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// read neighbouring warp's sum and add it to threads value |
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uint32_t blockSum = 0; |
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if (warp_id > 0) |
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{ |
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blockSum = sums[warp_id-1]; |
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} |
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value += blockSum; |
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// Now write out our result |
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data[id] = value; |
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// last thread has sum, write write out the block's sum |
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if (partial_sums != NULL && threadIdx.x == blockDim.x-1) |
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{ |
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partial_sums[blockIdx.x] = value; |
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} |
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} |
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// Uniform add: add partial sums array |
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__global__ void jackpot_compactTest_gpu_ADD(uint32_t *data, uint32_t *partial_sums, int len) |
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{ |
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__shared__ uint32_t buf; |
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int id = ((blockIdx.x * blockDim.x) + threadIdx.x); |
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if (id > len) return; |
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if (threadIdx.x == 0) |
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{ |
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buf = partial_sums[blockIdx.x]; |
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} |
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__syncthreads(); |
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data[id] += buf; |
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} |
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// Der Scatter |
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__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) |
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{ |
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int id = ((blockIdx.x * blockDim.x) + threadIdx.x); |
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uint32_t actNounce = id; |
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uint32_t value; |
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if (id < threads) |
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{ |
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// uint32_t nounce = startNounce + id; |
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uint32_t *inpHash; |
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if(d_validNonceTable == NULL) |
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{ |
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// keine Nonce-Liste |
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inpHash = &inpHashes[id<<4]; |
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}else |
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{ |
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// Nonce-Liste verfügbar |
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int nonce = d_validNonceTable[id] - startNounce; |
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actNounce = nonce; |
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inpHash = &inpHashes[nonce<<4]; |
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} |
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value = (*testFunc)(inpHash); |
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}else |
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{ |
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value = 0; |
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} |
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if( value ) |
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{ |
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int idx = sum[id]; |
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if(idx > 0) |
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outp[idx-1] = startNounce + actNounce; |
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} |
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} |
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__host__ static uint32_t jackpot_compactTest_roundUpExp(uint32_t val) |
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{ |
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if(val == 0) |
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return 0; |
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uint32_t mask = 0x80000000; |
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while( (val & mask) == 0 ) mask = mask >> 1; |
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if( (val & (~mask)) != 0 ) |
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return mask << 1; |
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return mask; |
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} |
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__host__ void jackpot_compactTest_cpu_singleCompaction(int thr_id, int threads, uint32_t *nrm, |
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uint32_t *d_nonces1, cuda_compactTestFunction_t function, |
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uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable) |
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{ |
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int orgThreads = threads; |
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threads = (int)jackpot_compactTest_roundUpExp((uint32_t)threads); |
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// threadsPerBlock ausrechnen |
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int blockSize = 256; |
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int nSummen = threads / blockSize; |
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int thr1 = (threads+blockSize-1) / blockSize; |
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int thr2 = threads / (blockSize*blockSize); |
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int blockSize2 = (nSummen < blockSize) ? nSummen : blockSize; |
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int thr3 = (nSummen + blockSize2-1) / blockSize2; |
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bool callThrid = (thr2 > 0) ? true : false; |
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// Erster Initialscan |
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jackpot_compactTest_gpu_SCAN<<<thr1,blockSize, 32*sizeof(uint32_t)>>>( |
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d_tempBranch1Nonces[thr_id], 32, d_partSum[0][thr_id], function, orgThreads, startNounce, inpHashes, d_validNonceTable); |
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// weitere Scans |
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if(callThrid) |
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{ |
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jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 32*sizeof(uint32_t)>>>(d_partSum[0][thr_id], 32, d_partSum[1][thr_id]); |
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jackpot_compactTest_gpu_SCAN<<<1, thr2, 32*sizeof(uint32_t)>>>(d_partSum[1][thr_id], (thr2>32) ? 32 : thr2); |
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}else |
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{ |
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jackpot_compactTest_gpu_SCAN<<<thr3,blockSize2, 32*sizeof(uint32_t)>>>(d_partSum[0][thr_id], (blockSize2>32) ? 32 : blockSize2); |
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} |
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// Sync + Anzahl merken |
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cudaStreamSynchronize(NULL); |
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if(callThrid) |
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cudaMemcpy(nrm, &(d_partSum[1][thr_id])[thr2-1], sizeof(uint32_t), cudaMemcpyDeviceToHost); |
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else |
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cudaMemcpy(nrm, &(d_partSum[0][thr_id])[nSummen-1], sizeof(uint32_t), cudaMemcpyDeviceToHost); |
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// Addieren |
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if(callThrid) |
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{ |
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jackpot_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum[0][thr_id]+blockSize, d_partSum[1][thr_id], blockSize*thr2); |
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} |
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jackpot_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch1Nonces[thr_id]+blockSize, d_partSum[0][thr_id], threads); |
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// Scatter |
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jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch1Nonces[thr_id], d_nonces1, |
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function, orgThreads, startNounce, inpHashes, d_validNonceTable); |
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// Sync |
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cudaStreamSynchronize(NULL); |
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} |
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////// ACHTUNG: Diese funktion geht aktuell nur mit threads > 65536 (Am besten 256 * 1024 oder 256*2048) |
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__host__ void jackpot_compactTest_cpu_dualCompaction(int thr_id, int threads, uint32_t *nrm, |
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uint32_t *d_nonces1, uint32_t *d_nonces2, |
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uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable) |
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{ |
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jackpot_compactTest_cpu_singleCompaction(thr_id, threads, &nrm[0], d_nonces1, h_JackpotTrueFunction[thr_id], startNounce, inpHashes, d_validNonceTable); |
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jackpot_compactTest_cpu_singleCompaction(thr_id, threads, &nrm[1], d_nonces2, h_JackpotFalseFunction[thr_id], startNounce, inpHashes, d_validNonceTable); |
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/* |
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// threadsPerBlock ausrechnen |
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int blockSize = 256; |
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int thr1 = threads / blockSize; |
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int thr2 = threads / (blockSize*blockSize); |
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// 1 |
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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); |
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jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 32*sizeof(uint32_t)>>>(d_partSum1[thr_id], 32, d_partSum2[thr_id]); |
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jackpot_compactTest_gpu_SCAN<<<1, thr2, 32*sizeof(uint32_t)>>>(d_partSum2[thr_id], (thr2>32) ? 32 : thr2); |
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cudaStreamSynchronize(NULL); |
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cudaMemcpy(&nrm[0], &(d_partSum2[thr_id])[thr2-1], sizeof(uint32_t), cudaMemcpyDeviceToHost); |
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jackpot_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum1[thr_id]+blockSize, d_partSum2[thr_id], blockSize*thr2); |
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jackpot_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch1Nonces[thr_id]+blockSize, d_partSum1[thr_id], threads); |
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// 2 |
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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); |
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jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 32*sizeof(uint32_t)>>>(d_partSum1[thr_id], 32, d_partSum2[thr_id]); |
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jackpot_compactTest_gpu_SCAN<<<1, thr2, 32*sizeof(uint32_t)>>>(d_partSum2[thr_id], (thr2>32) ? 32 : thr2); |
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cudaStreamSynchronize(NULL); |
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cudaMemcpy(&nrm[1], &(d_partSum2[thr_id])[thr2-1], sizeof(uint32_t), cudaMemcpyDeviceToHost); |
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jackpot_compactTest_gpu_ADD<<<thr2-1, blockSize>>>(d_partSum1[thr_id]+blockSize, d_partSum2[thr_id], blockSize*thr2); |
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jackpot_compactTest_gpu_ADD<<<thr1-1, blockSize>>>(d_tempBranch2Nonces[thr_id]+blockSize, d_partSum1[thr_id], threads); |
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// Hier ist noch eine Besonderheit: in d_tempBranch1Nonces sind die element von 1...nrm1 die Interessanten |
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// Schritt 3: Scatter |
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jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch1Nonces[thr_id], d_nonces1, h_JackpotTrueFunction[thr_id], threads, startNounce, inpHashes); |
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jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranch2Nonces[thr_id], d_nonces2, h_JackpotFalseFunction[thr_id], threads, startNounce, inpHashes); |
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cudaStreamSynchronize(NULL); |
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*/ |
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} |
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__host__ void jackpot_compactTest_cpu_hash_64(int thr_id, int threads, uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_validNonceTable, |
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uint32_t *d_nonces1, size_t *nrm1, |
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uint32_t *d_nonces2, size_t *nrm2, |
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int order) |
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{ |
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// Wenn validNonceTable genutzt wird, dann werden auch nur die Nonces betrachtet, die dort enthalten sind |
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// "threads" ist in diesem Fall auf die Länge dieses Array's zu setzen! |
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jackpot_compactTest_cpu_dualCompaction(thr_id, threads, |
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h_numValid[thr_id], d_nonces1, d_nonces2, |
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startNounce, inpHashes, d_validNonceTable); |
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cudaStreamSynchronize(NULL); // Das original braucht zwar etwas CPU-Last, ist an dieser Stelle aber evtl besser |
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*nrm1 = (size_t)h_numValid[thr_id][0]; |
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*nrm2 = (size_t)h_numValid[thr_id][1]; |
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}
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