Paradigms and Strategies for Scientific Computing on Distributed Memory Concurrent Computers Paradigms and Strategies for Scientific Computing on Distributed Memory Concurrent Computers 1

In this work we examine recent advances in parallel languages and abstractions that have the potential for improving the programmability and maintainability of large-scale, parallel, scientiic 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 method-ologies for parallelizing the PIC application are considered. The rst 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 eecient implementation .