// GPU version__global__voidmatMul(floatA[M][N],floatB[N][P],floatC[M][P]){introw = blockIdx.x * blockDim.x + threadIdx.x;intcol = blockIdx.y * blockDim.y + threadIdx.y;if(row < M && col < P) {floatC_value =0;for(inti =0; i < N; i++) {C_value += A[r...
// 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...
} // Thread block size #define BLOCK_SIZE 16 // 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 Mat...
根据矩阵运算CPU的代码,我们得到GPU运算的代码如下所示(详细源代码参看:MatMulKernel1D): https://github.com/CalvinXKY/BasicCUDA/blob/master/matrix_multiply/matMul1DKernel.cu __global__voidMatMulKernel1D(float*C,float*A,float*B,constintwh,constintwC,constint...
2006年,NVIDIA公司发布了CUDA(http://docs.nvidia.com/cuda/),CUDA是建立在NVIDIA的CPUs上的一个通用并行计算平台和编程模型,基于CUDA编程可以利用GPUs的并行计算引擎来更加高效地解决比较复杂的计算难题。近年来,GPU最成功的一个应用就是深度学习领域,基于GPU的并行计算已经成为训练深度学习模型的标配。目前,最新的CUDA...
根据矩阵运算CPU的代码,我们得到GPU运算的代码如下所示(详细源代码参看:MatMulKernel1D): __global__voidMatMulKernel1D(float*C,float*A,float*B,constintwh,constintwC,constinthC){constinttotalSize=wC*hC;intthID=threadIdx.x+blockIdx.x*blockDim.x;// 索引计算while(thID<totalSize){intCx=thID/wC;/...
Example python usage: providers = [("CUDAExecutionProvider", {"device_id": torch.cuda.current_device(), "user_compute_stream": str(torch.cuda.current_stream().cuda_stream)})] sess_options = ort.SessionOptions() sess = ort.InferenceSession("my_model.onnx", sess_options=sess_options, pro...
源码:MatMulKernel2DBlockMultiplesSize https:///CalvinXKY/BasicCUDA/blob/master/matrix_multiply/ 3.2 运算支持动态尺寸 在上述2D运算中,我们忽略一个问题,就是运算矩阵的长宽有可能不能够被Block整除,如下所示: 示例1:矩阵宽度经过M整除后,最后一个行块的宽度小于M; ...
NVIDIA CUDA Toolkit RN-06722-001 _v11.7 | 11 CUDA Libraries ‣ The IMMA kernels do not support padding in matrix C and may corrupt the data when matrix C with padding is supplied to cublasLtMatmul. A suggested work around is to supply matrix C with leading ...
The NVFORTRAN compiler can seamlessly accelerate many standard Fortran array intrinsics and language constructs including sum, maxval, minval, matmul, reshape, spread, and transpose on device and managed arrays by mapping Fortran statements to the functions available in the NVIDIA cuTENSOR library, a ...