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High Performance Fortran (HPF) has emerged as a standardlanguage fordata parallel computing. However, awide variety of scientific applications are best programmed by a combination of task and data parallelism. Therefore, a good model of taak parallelism is important for continued success of HPF for parallel progr amrning. This paper presents a taak parallelism model that is simple, elegant, and relatively easy to implement in an HPF environment. Task parallelism is exploited by mechanisms for dividing processors into subgroups and mapping computations and data onto processor subgroups. This model of taak parallelism has been implemented in the Fx compiler at Carnegie Mellon University. The paper addresses the main issues in compiling integrated task and data parallel programs and reports on the use of this model for programming various flat and nested task structures. Performance results are presented for a set of programs spanning signal processing, image processing, computer vision and environment modeling. A vaxiant of this task model is a new approved extension of HPF and this paper offers insight into the power of expression and ease of implementation of this extension.

Author

Jaspal Subhlok; Bwolen Yang

Publisher

ACM

Published

1997

Language

EN