针对基于PVM的桌面PC机联网而成的网络并行计算环境中,处理机的运算速度较快而处理机间的通信相对较慢,以及微机的内存有限的实际情况,从实用的角度出发,给出了基于PVM的网上求解有限元方程组的并行m-Step Jacob i PCG方法,该算法的矩...针对基于PVM的桌面PC机联网而成的网络并行计算环境中,处理机的运算速度较快而处理机间的通信相对较慢,以及微机的内存有限的实际情况,从实用的角度出发,给出了基于PVM的网上求解有限元方程组的并行m-Step Jacob i PCG方法,该算法的矩阵和向量采用行元素相邻单元贡献法实现有限元总体刚度矩阵和荷载向量的并行计算与组装,分块储存在各处理机上,其处理机间通信较少。并在1-4台桌面PC机连接成的局域网,PVM3.4 on W indow2000,VC 6.0并行计算平台上编程对该算法进行了数值试验,得到了较理想的结果。展开更多
In this paper we study the algorithms and their parallel implementation for solving large-scale generalized eigenvalue problems in modal analysis.Three predominant subspace algorithms,i.e.,Krylov-Schur method,implicit...In this paper we study the algorithms and their parallel implementation for solving large-scale generalized eigenvalue problems in modal analysis.Three predominant subspace algorithms,i.e.,Krylov-Schur method,implicitly restarted Arnoldi method and Jacobi-Davidson method,are modified with some complementary techniques to make them suitable for modal analysis.Detailed descriptions of the three algorithms are given.Based on these algorithms,a parallel solution procedure is established via the PANDA framework and its associated eigensolvers.Using the solution procedure on a machine equipped with up to 4800processors,the parallel performance of the three predominant methods is evaluated via numerical experiments with typical engineering structures,where the maximum testing scale attains twenty million degrees of freedom.The speedup curves for different cases are obtained and compared.The results show that the three methods are good for modal analysis in the scale of ten million degrees of freedom with a favorable parallel scalability.展开更多
文摘针对基于PVM的桌面PC机联网而成的网络并行计算环境中,处理机的运算速度较快而处理机间的通信相对较慢,以及微机的内存有限的实际情况,从实用的角度出发,给出了基于PVM的网上求解有限元方程组的并行m-Step Jacob i PCG方法,该算法的矩阵和向量采用行元素相邻单元贡献法实现有限元总体刚度矩阵和荷载向量的并行计算与组装,分块储存在各处理机上,其处理机间通信较少。并在1-4台桌面PC机连接成的局域网,PVM3.4 on W indow2000,VC 6.0并行计算平台上编程对该算法进行了数值试验,得到了较理想的结果。
基金supported by the National Defence Basic Fundamental Research Program of China(Grant No.C1520110002)the Fundamental Development Foundation of China Academy Engineering Physics(Grant No.2012A0202008)
文摘In this paper we study the algorithms and their parallel implementation for solving large-scale generalized eigenvalue problems in modal analysis.Three predominant subspace algorithms,i.e.,Krylov-Schur method,implicitly restarted Arnoldi method and Jacobi-Davidson method,are modified with some complementary techniques to make them suitable for modal analysis.Detailed descriptions of the three algorithms are given.Based on these algorithms,a parallel solution procedure is established via the PANDA framework and its associated eigensolvers.Using the solution procedure on a machine equipped with up to 4800processors,the parallel performance of the three predominant methods is evaluated via numerical experiments with typical engineering structures,where the maximum testing scale attains twenty million degrees of freedom.The speedup curves for different cases are obtained and compared.The results show that the three methods are good for modal analysis in the scale of ten million degrees of freedom with a favorable parallel scalability.
