摘要:

This paper presents a methodology for performance prediction of parallel algorithms and illustrates its use on a large scale computational chemistry application. The performance prediction uses a component time characterization technique which splits up the sequential code into computational components and measures the time for each of them. The parallel algorithm is built from these components by adding communication routines. A “Processor Activity Graph” (PAG) providing a graphical representation of the parallel algorithm runtime behaviour is used for predicting the execution time. For a case study a Self Consistent Field (SCF) computation has been selected which forms the basis of many computational chemistry packages [4, 5]. The performance model of SCF computation has been built and the prediction have been compared with the results of measurements. The measurements have been provided on a mesh connected distributed memory parallel computer (128 T800 Parsytec SuperCluster). The prediction error is less than 10%. Performance optimisation of the application has been achieved by reducing the communication overhead and changing the data representation.

ISSN:1063-7192    

DOI:10.1080/10637199608915612

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor

摘要:

In this paper we discuss the results of the parallel implementation of a new scheme for the solution of large and dense linear systems: an implicit version of the well known Gauss-Jordan method. We present a complete computational analysis of the method and we explain the superiority of implicit schemes on the basis of the number of accesses to the problem data.

ISSN:1063-7192    

DOI:10.1080/10637199608915613

出版商:Taylor & Francis Group    

年代:1996

数据来源: Taylor