The Parallel Scalability of the Spectral Transform Method

Ian Foster Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois

Search for other papers by Ian Foster in
Current site
Google Scholar
PubMed
Close
,
William Gropp Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois

Search for other papers by William Gropp in
Current site
Google Scholar
PubMed
Close
, and
Rick Stevens Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois

Search for other papers by Rick Stevens in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

This paper investigates the suitability of the spectral transform method for parallel implementation. The spectral transform method is a natural candidate for general circulation models (GCMs) designed to run on large-scale parallel computers due to the large number of existing serial and moderately parallel implementations. Analytic and empirical studies are presented that allow the parallel performance, and hence the scalability, of the spectral transform method to be quantified on different parallel computer architectures. Both the shallow-water equations and complete GCMs are considered. Results indicate that for the shallow-water equations, parallel efficiency is generally poor because of high communication requirements. It is predicted that for complete global climate models, the parallel efficiency will be significantly better; nevertheless, projected teraflop computers will have difficulty achieving acceptable throughput necessary for long-term regional climate studies.

Abstract

This paper investigates the suitability of the spectral transform method for parallel implementation. The spectral transform method is a natural candidate for general circulation models (GCMs) designed to run on large-scale parallel computers due to the large number of existing serial and moderately parallel implementations. Analytic and empirical studies are presented that allow the parallel performance, and hence the scalability, of the spectral transform method to be quantified on different parallel computer architectures. Both the shallow-water equations and complete GCMs are considered. Results indicate that for the shallow-water equations, parallel efficiency is generally poor because of high communication requirements. It is predicted that for complete global climate models, the parallel efficiency will be significantly better; nevertheless, projected teraflop computers will have difficulty achieving acceptable throughput necessary for long-term regional climate studies.

Save