Paradigms And Strategies For Scientific Computing On Distributed Memory Concurrent Computers

• Main Authors: Ian Foster, Ian T. Foster, David W. Walker
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: DISTRIBUTED MEMORY CONCURRENT COMPUTERS 1; Orca
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.7866

In this work we examine recent advances in parallel languages and abstractions that have the potential for improving the programmability and maintainability of largescale, parallel, scientific applications running on high performance architectures and networks. This paper focuses on Fortran M, a set of extensions to Fortran 77 that supports the modular design of message passing programs. We describe the Fortran M implementation of a particle-in-cell (PIC) plasma simulation application and discuss issues in the optimization of the code. The use of two other methodologies for parallelizing the PIC application are considered. The first is based on the shared object abstraction as embodied in the Orca language. The second approach is the Split-C language. In Fortran M, Orca, and Split-C the ability of the programmer to control the granularity of communication is important is designing an efficient implementation. 1. INTRODUCTION Distributed memory concurrent computers would appear to be i.