基于MPI和Taurus高性能计算系统的Jacobi并行迭代算法

Jacobi parallel iteration algorithms based on MPI and Taurus high performance computing system

针对Jacobi迭代的海量计算问题,设计了大规模并行计算算法。通过非阻塞通信函数替代阻塞通信函数、采用虚拟进程拓扑方式改进数据的区块划分,并利用高性能集群系统多计算节点协同处理对Jacobi并行迭代进行了尝试。实现了基于MPI的C语言串行与并行算法,利用Taurus HPC分别对串行、并行,单节点、多节点并行算法进行了系统测试。测试结果表明,进程间数据通信效率是影响并行程序性能的重要因素;跨多节点执行对于海量计算任务可显著提高计算速度;合理的数据区块划分有利于处理器的任务调度,可有效提高Jacobi并行迭代算法的执行效率。

Large-scale parallel computing algorithms have been designed for Jacobi iterative computing problems.Systematic improved the algorithm efficiency of Jacobi iteration by using a non-blocking communication function against blocking one,the topology method of virtual process,and the high performance computing system.The MPI-based C language serial and parallel algorithms were implemented,and the Taurus HPC was used to test the serial,parallel,single-node and multi-node parallel algorithms respectively.Experimental results shows that data communication efficiency between processes was an important factor of parallel program performance;across multiple computing nodes for compute-intensive tasks could significantly speed up the calculation;reasonable data block division was helpful for the task scheduling of the processors,which could greatly improve the parallel execution efficiency of Jacobi iteration algorithms.