我正在尝试编写一个简单的矩阵乘法程序，该程序不断将两个矩阵的乘积添加到第三个结果矩阵（我实际上是在使用单独的设备测量功耗时对GPU进行锻炼）。
当我指定大量迭代时，会出现问题。我已经尝试了BLOCK_SIZE和矩阵维度值的几种组合，并且我注意到迭代次数可以用更小的矩阵维度来增加，但是BLOCK_SIZE必须是矩阵维度的平方根（方阵）。
在这种情况下产生的错误是39秒（无论迭代值，只要它'太多'）冻结，然后是所有零矩阵输出。有趣的是，我运行了一次，迭代次数为20000，运行正常。我再次运行它并得到了冻结错误。
有什么想法吗？提前谢谢！
内核：
//********************************************************************
// matrixMultiplication_kernel.cu
//
// Kernel for a basic CUDA matrix multiplication program.
//********************************************************************
#ifndef MATRIXMULTIPLICATION_KERNEL
#define MATRIXMULTIPLICATION_KERNEL
#define BLOCK_SIZE 16 // Set thread block size
#define colsA 256 // Set matrix A column dimension
#define rowsA 256 // Set matrix A row dimension
#define colsB 256 // Set matrix B column dimension
#define rowsB colsA // Set matrix B row dimension
#define colsC colsB // Set matrix C column dimension
#define rowsC rowsA // Set matrix C row dimension
//--------------------------------------------------------------------
// matrixMultiplication() - Multiplies matrixA and matrixB, storing
// the result in device memory for matrixC.
//
// PRE: matrixA, matrixB, and matrixC are float pointers; numColsA
// numColsB are integers.
// POST: The result of multiplying matrixA and matrixB is stored in
// matrixC.
//--------------------------------------------------------------------
__global__ void matrixMultiplication(float * matrixA, float * matrixB,
float * matrixC, int numColsA,
int numColsB) {
/* Declare matrix-multplication holder value ouside of for loop */
float val;
/* Set block and thread index positions */
int blockX = blockIdx.x;
int blockY = blockIdx.y;
int threadX = threadIdx.x;
int threadY = threadIdx.y;
/*
Set starting and ending indices of the first sub-matrix of A
and sub-matrix size for matrix A
*/
int startA = numColsA * BLOCK_SIZE * blockY;
int endA = startA + numColsA - 1;
int subSizeA = BLOCK_SIZE;
/*
Set starting index of the first sub-matrix of B and sub-matrix
size for matrix B
*/
int startB = BLOCK_SIZE * blockX;
int subSizeB = BLOCK_SIZE * colsB;
/* Perform matrix multiplication 20000 times */
for (int iteration = 0; iteration < 20000; iteration++) {
/* Loop through matrix A and matrix B's sub-matrices */
for (int i = startA, j = startB; i <= endA; i += subSizeA,
j += subSizeB) {
/*
Declare shared memory arrays for matrix A and B
sub-matrices
*/
__shared__ float subA[BLOCK_SIZE][BLOCK_SIZE];
__shared__ float subB[BLOCK_SIZE][BLOCK_SIZE];
/* Fill sub-matrices */
subA[threadY][threadX] =
matrixA[i + colsA * threadY + threadX];
subB[threadY][threadX] =
matrixB[j + colsB * threadY + threadX];
/* Ensure that the matrices are loaded */
__syncthreads();
/* Loop through the block */
for (int k = 0; k < BLOCK_SIZE; ++k) {
/* Compute product of two matrix indices */
val += subA[threadY][k] * subB[k][threadX];
}
/*
Ensure completion before the next set of sub-matrices
begin computation
*/
__syncthreads();
}
/* Set device memory for this sub-matrix */
int position = colsB * BLOCK_SIZE * blockY + BLOCK_SIZE * blockX;
matrixC[position + colsB * threadY + threadX] = val;
}
}
#endif
主机：
//********************************************************************
// matrixMultiplication.cu
//
// A basic CUDA matrix multiplication program.
//********************************************************************
/* Include necessary libraries and kernel */
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <matrixMultiplication_kernel.cu>
/* Function declarations */
void fillMatrix(float * matrix, int numIndices);
//*************
// Main Program
//*************
int main(int argc, char** argv) {
/* Declare device memory */
float * deviceA;
float * deviceB;
float * deviceC;
srand(2013); // Set random seed
/* Determine total number of indices in each matrix */
unsigned int numIndicesA = colsA * rowsA;
unsigned int numIndicesB = colsB * rowsB;
unsigned int numIndicesC = colsC * rowsC;
/* Determine memory size of each matrix */
unsigned int memoryA = sizeof(float) * numIndicesA;
unsigned int memoryB = sizeof(float) * numIndicesB;
unsigned int memoryC = sizeof(float) * numIndicesC;
/* Allocate memory for each matrix */
float * matrixA = (float *) malloc(memoryA);
float * matrixB = (float *) malloc(memoryB);
float * matrixC = (float *) malloc(memoryC);
/* Set contents of matrices A and B (matrix C is all zeros) */
fillMatrix(matrixA, numIndicesA);
fillMatrix(matrixB, numIndicesB);
/* Allocate device memory for each matrix */
cudaMalloc((void **) &deviceA, memoryA);
cudaMalloc((void **) &deviceB, memoryB);
cudaMalloc((void **) &deviceC, memoryC);
/* Copy host memory to device memory for matrices A and B */
cudaMemcpy(deviceA, matrixA, memoryA, cudaMemcpyHostToDevice);
cudaMemcpy(deviceB, matrixB, memoryB, cudaMemcpyHostToDevice);
/* Set thread count to BLOCK_SIZE x BLOCK_SIZE */
dim3 tCount(BLOCK_SIZE, BLOCK_SIZE);
/* Set thread block count */
dim3 tbCount((colsC / tCount.x), (rowsC / tCount.y));
/* Run kernel */
matrixMultiplication <<< tbCount, tCount >>> (deviceA, deviceB,
deviceC, colsA,
colsB);
/* Copy device memory to host memory for matrix C */
cudaMemcpy(matrixC, deviceC, memoryC, cudaMemcpyDeviceToHost);
for(int i = 0; i < 256; i++) {
printf("%f ", matrixC[i]);
}
printf("\n");
/* Free up host and device memory for each matrix */
free(matrixA);
free(matrixB);
free(matrixC);
cudaFree(deviceA);
cudaFree(deviceB);
cudaFree(deviceC);
}
//--------------------------------------------------------------------
// fillMatrix - Assigns a random float value to each indice of the
// matrix.
//
// PRE: matrix is a pointer to a block of bytes in memory; numIndices
// is the number of indicies in the matrix being instantiated.
// POST: Each index of the matrix has been filled with random float
// values.
//--------------------------------------------------------------------
void fillMatrix(float * matrix, int numIndices) {
/* Loop through each index of the matrix */
for (int i = 0; i < numIndices; ++i) {
/*
Assign a random float between 0 and 1 for this index of
the matrix
*/
matrix[i] = rand() / (float)RAND_MAX;
}
}
生成文件：
GCC = nvcc
CUDA_INSTALL_PATH := /usr/local/cuda
INCLUDES := -I. -I$(CUDA_INSTALL_PATH)/include
CUDA_LIBS := -L$(CUDA_INSTALL_PATH)/lib -lcudart
matrixMultiplication.o: matrixMultiplication.cu
$(GCC) $(INCLUDES) -c matrixMultiplication.cu -o $@
matrixMultiplication: matrixMultiplication.o
$(GCC) -o $@ matrixMultiplication.o $(CUDA_LIBS)
clean:
$(RM) *.o *~
答案 0 :(得分：1)
问题解决了！由于内核的持续时间很长，这是一个系统超时问题。通过切换仅终端模式，我能够绕过这个问题。
感谢所有帮助人员！