THE PARALLEL COMPUTATION OF PARTIAL EIGENSOLUTIONS USING A MODIFIED LANCZOS METHOD

作     者：K. Murphy[a] M. Clint[a] M. Szularz[b] J. Weston[b] 

作者机构：[a] Department of Computer Science The Queen's University of Belfast Belfast U.K [b] School of Information and Software Engineering The University of Ulster at Coleraine Coleraine U.K 

出 版 物：《Parallel Algorithms and Applications》 (并行、紧急、分布式系统国际杂志)

年 卷 期：1997年第11卷第3-4期

页      面：299-323页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Lanczos algorithm convergence monitoring orthogonalization high performance computing 

摘      要：The Lanczos algorithm is one of the most widely used methods for finding a small number of the extremal eigenvalues and associated eigenvectors of large, sparse, symmetric matrices. In this paper the performance on two parallel machines with different architectures of a modified version of the algorithm which incorporates a novel convergence monitoring method is assessed. The investigation has been carried out using a shared memory Convex C3840 with two processors and a 16-node Intel iPSC/860 hypercube. It is shown that parallel implementations of the modified algorithm can efficiently exploit the facilities provided by both machines. However, there are significant architecture dependent considerations which favour the use of the shared memory machine for the solution of general instances of the problem. These considerations relate to the cost of inter-processor communication and the limited availability of fast memory on the distributed memory machine.