Parallel matrix multiplication based on MPI+CUDA asynchronous model

Matrix multiplication plays an important role in scientific computing.Different structural models can improve the performance of parallel matrix multiplication.In the existing MPI+CUDA synchronization model,the host-side need enter the waiting state and cannot continue to work until the device completes the task,which obviously wastes time.Concerning this question,a parallel matrix multiplication based on MPI+CUDA asynchronous model was proposed.This model prevented host-side's entering into the waiting state,and used CUDA-stream technology to solve the problem of data bulk over GPU memory.By analyzing the speedup ratio and efficiency of the asynchronous model,the experimental results show that MPI+CUDA parallel programming obviously promotes parallel efficiency and large-scale matrix multiplication's speed,which exerts the advantages of the distributional memory between the nodes and the share memory in the node.It is an effective and feasible parallel strategy.