bump to revision 0.7

JHA/cuda_jha_compactionTest.cu

@@ -0,0 +1,255 @@
+#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_tempBranch2Nonces[8];
+static size_t *d_numValid[8];
+static size_t *h_numValid[8];
+
+static uint32_t *d_partSum1[8], *d_partSum2[8]; // 2x partielle summen
+static uint32_t *d_validTemp1[8], *d_validTemp2[8];
+
+// Zwischenspeicher
+static uint32_t *d_tempBranchAllNonces[8];
+
+// aus heavy.cu
+extern cudaError_t MyStreamSynchronize(cudaStream_t stream, int situation, int thr_id);
+
+
+
+// Setup-Funktionen
+__host__ void jackpot_compactTest_cpu_init(int thr_id, int threads)
+{
+	cudaGetDeviceProperties(&props[thr_id], device_map[thr_id]);
+
+	// wir brauchen auch Speicherplatz auf dem Device
+	cudaMalloc(&d_tempBranchAllNonces[thr_id], sizeof(uint32_t) * threads);
+	cudaMalloc(&d_tempBranch1Nonces[thr_id], sizeof(uint32_t) * threads);
+	cudaMalloc(&d_tempBranch2Nonces[thr_id], sizeof(uint32_t) * threads);
+	cudaMalloc(&d_numValid[thr_id], 2*sizeof(size_t));
+	cudaMallocHost(&h_numValid[thr_id], 2*sizeof(size_t));
+
+	uint32_t s1;
+	s1 = threads / 256;
+
+	cudaMalloc(&d_partSum1[thr_id], sizeof(uint32_t) * s1); // BLOCKSIZE (Threads/Block)
+	cudaMalloc(&d_partSum2[thr_id], sizeof(uint32_t) * s1); // BLOCKSIZE (Threads/Block)
+
+	cudaMalloc(&d_validTemp1[thr_id], sizeof(uint32_t) * threads); // BLOCKSIZE (Threads/Block)
+	cudaMalloc(&d_validTemp2[thr_id], sizeof(uint32_t) * threads); // BLOCKSIZE (Threads/Block)
+}
+
+// Die Testfunktion (zum Erstellen der TestMap)
+__global__ void jackpot_compactTest_gpu_TEST_64(int threads, uint32_t startNounce, uint32_t *inpHashes, uint32_t *d_noncesFull,
+												uint32_t *d_nonces1, uint32_t *d_nonces2,
+												uint32_t *d_validT1, uint32_t *d_validT2)
+{
+	int thread = (blockDim.x * blockIdx.x + threadIdx.x);
+	if (thread < threads)
+	{
+		// bestimme den aktuellen Zähler
+		uint32_t nounce = startNounce + thread;
+		uint32_t *inpHash = &inpHashes[16 * thread];
+
+		uint32_t tmp = inpHash[0] & 0x01;
+		uint32_t val1 = (tmp == 1);
+		uint32_t val2 = (tmp == 0);
+
+		d_nonces1[thread] = val1;
+		d_validT1[thread] = val1;
+		d_nonces2[thread] = val2;
+		d_validT2[thread] = val2;
+		d_noncesFull[thread] = nounce;
+	}
+}
+
+// Die Summenfunktion (vom NVIDIA SDK)
+__global__ void jackpot_compactTest_gpu_SCAN(uint32_t *data, int width, uint32_t *partial_sums=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;
+
+	// 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 = data[id];
+
+	// 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 *data, uint32_t *valid, uint32_t *sum, uint32_t *outp)
+{
+	int id = ((blockIdx.x * blockDim.x) + threadIdx.x);
+	if( valid[id] )
+	{
+		int idx = sum[id];
+		if(idx > 0)
+			outp[idx-1] = data[id];
+	}
+}
+
+////// 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, size_t *nrm,
+													 uint32_t *d_nonces1, uint32_t *d_nonces2)
+{
+	// threadsPerBlock ausrechnen
+	int blockSize = 256;
+	int thr1 = threads / blockSize;
+	int thr2 = threads / (blockSize*blockSize);
+
+	// 1
+	jackpot_compactTest_gpu_SCAN<<<thr1,blockSize, 8*sizeof(uint32_t)>>>(d_tempBranch1Nonces[thr_id], 32, d_partSum1[thr_id]);
+	jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 8*sizeof(uint32_t)>>>(d_partSum1[thr_id], 32, d_partSum2[thr_id]);
+	jackpot_compactTest_gpu_SCAN<<<1, thr2, 8*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, 8*sizeof(uint32_t)>>>(d_tempBranch2Nonces[thr_id], 32, d_partSum1[thr_id]);
+	jackpot_compactTest_gpu_SCAN<<<thr2,blockSize, 8*sizeof(uint32_t)>>>(d_partSum1[thr_id], 32, d_partSum2[thr_id]);
+	jackpot_compactTest_gpu_SCAN<<<1, thr2, 8*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_tempBranchAllNonces[thr_id], d_validTemp1[thr_id], d_tempBranch1Nonces[thr_id], d_nonces1);
+	jackpot_compactTest_gpu_SCATTER<<<thr1,blockSize,0>>>(d_tempBranchAllNonces[thr_id], d_validTemp2[thr_id], d_tempBranch2Nonces[thr_id], d_nonces2);
+	cudaStreamSynchronize(NULL);
+}
+
+__host__ void jackpot_compactTest_cpu_hash_64(int thr_id, int threads, uint32_t startNounce, uint32_t *inpHashes, 
+											uint32_t *d_nonces1, size_t *nrm1,
+											uint32_t *d_nonces2, size_t *nrm2,
+											int order)
+{
+	// Compute 3.x und 5.x Geräte am besten mit 768 Threads ansteuern,
+	// alle anderen mit 512 Threads.
+	//int threadsperblock = (props[thr_id].major >= 3) ? 768 : 512;
+	int threadsperblock = 256;
+
+	// berechne wie viele Thread Blocks wir brauchen
+	dim3 grid((threads + threadsperblock-1)/threadsperblock);
+	dim3 block(threadsperblock);
+
+	size_t shared_size = 0;
+
+//	fprintf(stderr, "threads=%d, %d blocks, %d threads per block, %d bytes shared\n", threads, grid.x, block.x, shared_size);
+
+	// Schritt 1: Prüfen der Bedingung und Speicherung in d_tempBranch1/2Nonces
+	jackpot_compactTest_gpu_TEST_64<<<grid, block, shared_size>>>(threads, startNounce, inpHashes, d_tempBranchAllNonces[thr_id],
+		d_tempBranch1Nonces[thr_id], d_tempBranch2Nonces[thr_id],
+		d_validTemp1[thr_id], d_validTemp2[thr_id]);
+
+	// Strategisches Sleep Kommando zur Senkung der CPU Last
+	jackpot_compactTest_cpu_dualCompaction(thr_id, threads,
+		h_numValid[thr_id], d_nonces1, d_nonces2);
+
+	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];
+}

JHA/cuda_jha_keccak512.cu

@@ -1,3 +1,5 @@
+
+
 #include <cuda.h>
 #include "cuda_runtime.h"
 #include "device_launch_parameters.h"
@@ -132,7 +134,7 @@ keccak_block(uint64_t *s, const uint32_t *in, const uint64_t *keccak_round_const
     }
 }
 
-__global__ void jackpot_keccak512_gpu_hash_88(int threads, uint32_t startNounce, uint64_t *g_hash)
+__global__ void jackpot_keccak512_gpu_hash(int threads, uint32_t startNounce, uint64_t *g_hash)
 {
     int thread = (blockDim.x * blockIdx.x + threadIdx.x);
     if (thread < threads)
@@ -518,9 +520,9 @@ void KeccakF( tKeccakLane * state, const tKeccakLane *in, int laneCount )
     }
 }
 
-__host__ void jackpot_keccak512_cpu_setBlock_88(void *pdata)
+// inlen kann 72...143 betragen
+__host__ void jackpot_keccak512_cpu_setBlock(void *pdata, size_t inlen)
 {
-    unsigned long long inlen = 88;
     const unsigned char *in = (const unsigned char*)pdata;
 
     tKeccakLane    state[5 * 5];
@@ -554,7 +556,7 @@ __host__ void jackpot_keccak512_cpu_setBlock_88(void *pdata)
                         0, cudaMemcpyHostToDevice);
 }
 
-__host__ void jackpot_keccak512_cpu_hash_88(int thr_id, int threads, uint32_t startNounce, uint32_t *d_hash, int order)
+__host__ void jackpot_keccak512_cpu_hash(int thr_id, int threads, uint32_t startNounce, uint32_t *d_hash, int order)
 {
     const int threadsperblock = 256;
 
@@ -567,6 +569,6 @@ __host__ void jackpot_keccak512_cpu_hash_88(int thr_id, int threads, uint32_t st
 
 //    fprintf(stderr, "threads=%d, %d blocks, %d threads per block, %d bytes shared\n", threads, grid.x, block.x, shared_size);
 
-    jackpot_keccak512_gpu_hash_88<<<grid, block, shared_size>>>(threads, startNounce, (uint64_t*)d_hash);
+    jackpot_keccak512_gpu_hash<<<grid, block, shared_size>>>(threads, startNounce, (uint64_t*)d_hash);
     MyStreamSynchronize(NULL, order, thr_id);
 }