Sample CUDA Program /* * NVIDIA CUDA matrix multiply example straight out of the CUDA * programming manual, more or less. * * $Id: tx_cuda_matmul.cu,v 1.1.4.1 2010/03/10 14:58:09 jdelsign Exp $ */ #include <cuda.h> #include <stdio.h> // Matrices are stored in row-major order: // M(row, col) = *(M.elements + row * M.stride + col) typedef struct { int width; /* number of columns */ int height; /* number of rows */ int stride; float* elements; } Matrix; // Get a matrix element __device__ float GetElement(const Matrix A, int row, int col) { return A.elements[row * A.stride + col]; } // Set a matrix element __device__ void SetElement(Matrix A, int row, int col, float value) { A.elements[row * A.stride + col] = value; } // Thread block size #define BLOCK_SIZE 2 // Get the BLOCK_SIZExBLOCK_SIZE sub-matrix Asub of A that is // located col sub-matrices to the right and row sub-matrices down // from the upper-left corner of A __device__ Matrix GetSubMatrix(Matrix A, int row, int col) { Matrix Asub; Asub.width = BLOCK_SIZE; Asub.height = BLOCK_SIZE; Asub.stride = A.stride; Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row + BLOCK_SIZE * col]; return Asub; } // Forward declaration of the matrix multiplication kernel __global__ void MatMulKernel(const Matrix, const Matrix, Matrix); // Matrix multiplication - Host code // Matrix dimensions are assumed to be multiples of BLOCK_SIZE void MatMul(const Matrix A, const Matrix B, Matrix C) { // Load A and B to device memory Matrix d_A; d_A.width = d_A.stride = A.width; d_A.height = A.height; size_t size = A.width * A.height * sizeof(float); cudaMalloc((void**)&d_A.elements, size); cudaMemcpy(d_A.elements, A.elements, size, cudaMemcpyHostToDevice); Matrix d_B; d_B.width = d_B.stride = B.width; d_B.height = B.height; size = B.width * B.height * sizeof(float); cudaMalloc((void**)&d_B.elements, size); cudaMemcpy(d_B.elements, B.elements, size, cudaMemcpyHostToDevice); // Allocate C in device memory Matrix d_C; d_C.width = d_C.stride = C.width; d_C.height = C.height; size = C.width * C.height * sizeof(float); cudaMalloc((void**)&d_C.elements, size); // Invoke kernel dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE); dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y); MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C); // Read C from device memory cudaMemcpy(C.elements, d_C.elements, size, cudaMemcpyDeviceToHost); // Free device memory cudaFree(d_A.elements); cudaFree(d_B.elements); cudaFree(d_C.elements); } // Matrix multiplication kernel called by MatrixMul() __global__ void MatMulKernel(Matrix A, Matrix B, Matrix C) { // Block row and column int blockRow = blockIdx.y; int blockCol = blockIdx.x; // Each thread block computes one sub-matrix Csub of C Matrix Csub = GetSubMatrix(C, blockRow, blockCol); // Each thread computes one element of Csub // by accumulating results into Cvalue float Cvalue = 0; // Thread row and column within Csub int row = threadIdx.y; int col = threadIdx.x; // Loop over all the sub-matrices of A and B that are // required to compute Csub // Multiply each pair of sub-matrices together // and accumulate the results for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) { // Get sub-matrix Asub of A Matrix Asub = GetSubMatrix(A, blockRow, m); // Get sub-matrix Bsub of B Matrix Bsub = GetSubMatrix(B, m, blockCol); // Shared memory used to store Asub and Bsub respectively __shared__ float As[BLOCK_SIZE][BLOCK_SIZE]; __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE]; // Load Asub and Bsub from device memory to shared memory // Each thread loads one element of each sub-matrix As[row][col] = GetElement(Asub, row, col); Bs[row][col] = GetElement(Bsub, row, col); // Synchronize to make sure the sub-matrices are loaded // before starting the computation __syncthreads(); // Multiply Asub and Bsub together for (int e = 0; e < BLOCK_SIZE; ++e) Cvalue += As[row][e] * Bs[e][col]; // Synchronize to make sure that the preceding // computation is done before loading two new // sub-matrices of A and B in the next iteration __syncthreads(); } // Write Csub to device memory // Each thread writes one element SetElement(Csub, row, col, Cvalue); // Just a place to set a breakpoint in the debugger __syncthreads(); __syncthreads(); /* STOP: Csub should be fully updated */ } static Matrix cons_Matrix (int width_, int height_) { Matrix A; A.width = width_; A.height = height_; A.stride = width_; A.elements = (float*) malloc(sizeof(*A.elements) * width_ * height_); for (int row = 0; row < height_; row++) for (int col = 0; col < width_; col++) A.elements[row * width_ + col] = row * 10.0 + col; return A; } static void print_Matrix (Matrix A, char *name) { printf("%s:\n", name); for (int row = 0; row < A.height; row++) for (int col = 0; col < A.width; col++) printf ("[%5d][%5d] %f\n", row, col, A.elements[row * A.stride + col]); } // Multiply an m*n matrix with an n*p matrix results in an m*p matrix. // Usage: tx_cuda_matmul [ m [ n [ p ] ] ] // m, n, and p default to 1, and are multiplied by BLOCK_SIZE. int main(int argc, char **argv) { // cudaSetDevice(0); const int m = BLOCK_SIZE * (argc > 1 ? atoi(argv[1]) : 1); const int n = BLOCK_SIZE * (argc > 2 ? atoi(argv[2]) : 1); const int p = BLOCK_SIZE * (argc > 3 ? atoi(argv[3]) : 1); Matrix A = cons_Matrix(m, n); Matrix B = cons_Matrix(n, p); Matrix C = cons_Matrix(m, p); MatMul(A, B, C); print_Matrix(A, "A"); print_Matrix(B, "B"); print_Matrix(C, "C"); return 0; }