在这个作业中，我需要使用CUDA C完善代码来计算两个矩形矩阵的乘积。完成代码后，我提交了答案，当矩阵是方形矩阵时，数据集的解决方案是正确的，但当矩阵不是方形矩阵时，结果与预期值不匹配。
以下是我添加缺失部分后的代码：
以下是我添加缺失部分后的代码：
#include <wb.h>
#define wbCheck(stmt) do { \
cudaError_t err = stmt; \
if (err != cudaSuccess) { \
wbLog(ERROR, "Failed to run stmt ", #stmt); \
return -1; \
} \
} while(0)
// Compute C = A * B
__global__ void matrixMultiply(float * A, float * B, float * C,
int numARows, int numAColumns,
int numBRows, int numBColumns,
int numCRows, int numCColumns) {
//@@ Insert code to implement matrix multiplication here
int Row = blockIdx.y * blockDim.y + threadIdx.y;
int Col = blockIdx.x * blockDim.x + threadIdx.x;
if (numAColumns != numBRows) return ;
if ((Row < numARows) && (Col < numBColumns)){
float Cvalue = 0;
for (int k = 0 ; k < numAColumns ; ++k )
Cvalue += A[Row*numAColumns + k] * B[k * numBRows + Col];
C[Row*numAColumns + Col] = Cvalue;
}
}
int main(int argc, char ** argv) {
wbArg_t args;
float * hostA; // The A matrix
float * hostB; // The B matrix
float * hostC; // The output C matrix
float * deviceA;
float * deviceB;
float * deviceC;
int numARows; // number of rows in the matrix A
int numAColumns; // number of columns in the matrix A
int numBRows; // number of rows in the matrix B
int numBColumns; // number of columns in the matrix B
int numCRows; // number of rows in the matrix C (you have to set this)
int numCColumns; // number of columns in the matrix C (you have to set this)
args = wbArg_read(argc, argv);
wbTime_start(Generic, "Importing data and creating memory on host");
hostA = (float *) wbImport(wbArg_getInputFile(args, 0), &numARows, &numAColumns);
hostB = (float *) wbImport(wbArg_getInputFile(args, 1), &numBRows, &numBColumns);
//@@ Set numCRows and numCColumns
numCRows = 0;
numCColumns = 0;
numCRows = numARows;
numCColumns = numBColumns;
//@@ Allocate the hostC matrix
hostC = (float*) malloc(sizeof(float)*numCRows*numCColumns);
wbTime_stop(Generic, "Importing data and creating memory on host");
wbLog(TRACE, "The dimensions of A are ", numARows, " x ", numAColumns);
wbLog(TRACE, "The dimensions of B are ", numBRows, " x ", numBColumns);
wbTime_start(GPU, "Allocating GPU memory.");
//@@ Allocate GPU memory here
cudaMalloc((void**)&deviceA ,sizeof(float)*numARows*numAColumns );
cudaMalloc((void**)&deviceB , sizeof(float)*numBRows*numBColumns);
cudaMalloc((void**)&deviceC , sizeof(float)*numCRows*numCColumns);
wbTime_stop(GPU, "Allocating GPU memory.");
wbTime_start(GPU, "Copying input memory to the GPU.");
//@@ Copy memory to the GPU here
cudaMemcpy(deviceA, hostA, sizeof(float)*numARows*numAColumns, cudaMemcpyHostToDevice);
cudaMemcpy(deviceB, hostB, sizeof(float)*numBRows*numBColumns, cudaMemcpyHostToDevice);
wbTime_stop(GPU, "Copying input memory to the GPU.");
//@@ Initialize the grid and block dimensions here
dim3 DimGrid(numARows / 8 , numBColumns / 8, 1);
dim3 DimBlock(8 , 8, 1);
wbTime_start(Compute, "Performing CUDA computation");
//@@ Launch the GPU Kernel here
matrixMultiply<<<DimGrid , DimBlock>>>(deviceA , deviceB , deviceC , numARows , numAColumns, numBRows ,numBColumns , numCRows , numCColumns);
cudaThreadSynchronize();
wbTime_stop(Compute, "Performing CUDA computation");
wbTime_start(Copy, "Copying output memory to the CPU");
//@@ Copy the GPU memory back to the CPU here
cudaMemcpy(hostC, deviceC, sizeof(float)*numCRows*numCColumns , cudaMemcpyDeviceToHost);
wbTime_stop(Copy, "Copying output memory to the CPU");
wbTime_start(GPU, "Freeing GPU Memory");
//@@ Free the GPU memory here
cudaFree(deviceA);
cudaFree(deviceB);
cudaFree(deviceC);
wbTime_stop(GPU, "Freeing GPU Memory");
wbSolution(args, hostC, numCRows, numCColumns);
free(hostA);
free(hostB);
free(hostC);
return 0;
}
我希望你能帮我找出哪一部分是不正确的。