手写一个使用CUDA实现的矩阵乘法需要编写CUDA C代码，然后通过Python调用它。下面是一个简单的例子，演示如何使用CUDA实现矩阵乘法，并在Python中使用它。

CUDA C代码

首先，编写CUDA C代码来实现矩阵乘法。保存为matrix_mul.cu：

#include <cuda_runtime.h>#include <stdio.h>__global__ void matrixMulKernel(float* A, float* B, float* C, int N) {    int row = blockIdx.y * blockDim.y + threadIdx.y;    int col = blockIdx.x * blockDim.x + threadIdx.x;        if (row < N && col < N) {        float value = 0;        for (int k = 0; k < N; ++k) {            value += A[row * N + k] * B[k * N + col];        }        C[row * N + col] = value;    }}extern "C" void matrixMul(float* A, float* B, float* C, int N) {    float *d_A, *d_B, *d_C;    size_t size = N * N * sizeof(float);    cudaMalloc(&d_A, size);    cudaMalloc(&d_B, size);    cudaMalloc(&d_C, size);    cudaMemcpy(d_A, A, size, cudaMemcpyHostToDevice);    cudaMemcpy(d_B, B, size, cudaMemcpyHostToDevice);    dim3 threadsPerBlock(16, 16);    dim3 numBlocks((N + threadsPerBlock.x - 1) / threadsPerBlock.x,                   (N + threadsPerBlock.y - 1) / threadsPerBlock.y);    matrixMulKernel<<<numBlocks, threadsPerBlock>>>(d_A, d_B, d_C, N);    cudaMemcpy(C, d_C, size, cudaMemcpyDeviceToHost);    cudaFree(d_A);    cudaFree(d_B);    cudaFree(d_C);}

使用nvcc编译CUDA代码为共享库：

nvcc -o matrix_mul.so --shared -Xcompiler -fPIC matrix_mul.cu

然后，在Python中调用这个CUDA实现。确保安装了numpy和ctypes模块。

import numpy as npimport ctypes# Load the compiled shared librarymatrix_mul = ctypes.CDLL('./matrix_mul.so')# Define the argument types for the functionmatrix_mul.matrixMul.argtypes = [    ctypes.POINTER(ctypes.c_float),  # A    ctypes.POINTER(ctypes.c_float),  # B    ctypes.POINTER(ctypes.c_float),  # C    ctypes.c_int                     # N]def cuda_matrix_mul(A, B):    N = A.shape[0]    C = np.zeros((N, N), dtype=np.float32)    # Convert numpy arrays to ctypes pointers    A_ptr = A.ctypes.data_as(ctypes.POINTER(ctypes.c_float))    B_ptr = B.ctypes.data_as(ctypes.POINTER(ctypes.c_float))    C_ptr = C.ctypes.data_as(ctypes.POINTER(ctypes.c_float))    # Call the CUDA function    matrix_mul.matrixMul(A_ptr, B_ptr, C_ptr, N)    return C# Example usageA = np.array([[1, 2], [3, 4]], dtype=np.float32)B = np.array([[5, 6], [7, 8]], dtype=np.float32)C = cuda_matrix_mul(A, B)print(C)