A New Model for Integrated Nested Task and Data Parallel Programming

		     Jaspal Subhlok and Bwolen Yang

Abstract: High Performance Fortran (HPF) has emerged as a standard
language for data parallel computing. However, a wide variety of
scientific applications are best programmed by a combination of task and
data parallelism.  Therefore, a good model of task parallelism is
important for continued success of HPF for parallel programming. This
paper presents a task 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
task 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 variant 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.

@inproceedings{ppopp97task,
title = "A New Model for Integrated Nested Task and Data Parallel Programming",
author = "J. Subhlok and B. Yang",
booktitle = "Proc. of the Sixth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP)" ,
month = jun,
address = "Las Vegas, NV",
year = "1997"
}