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Optimistic parallelism requires abstractions

Optimistic parallelism requires abstractions The problem of writing software for multicore processors is greatly simplified if we could automatically parallelize sequential programs. Although auto-parallelization has been studied for many decades, it has succeeded only in a few application areas such as dense matrix computations. In particular, auto-parallelization of irregular programs, which are organized around large, pointer-based data structures like graphs, has seemed intractable. The Galois project is taking a fresh look at autoparallelization. Rather than attempt to parallelize all programs no matter how obscurely they are written, we are designing programming abstractions that permit programmers to highlight opportunities for exploiting parallelism in sequential programs, and building a runtime system that uses these hints to execute the program in parallel. In this paper, we describe the design and implementation of a system based on these ideas. Experimental results for two real-world irregular applications, a Delaunay mesh refinement application and a graphics application that performs agglomerative clustering, demonstrate that this approach is promising. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications of the ACM Association for Computing Machinery

Optimistic parallelism requires abstractions

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
ISSN
0001-0782
DOI
10.1145/1562164.1562188
Publisher site
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Abstract

The problem of writing software for multicore processors is greatly simplified if we could automatically parallelize sequential programs. Although auto-parallelization has been studied for many decades, it has succeeded only in a few application areas such as dense matrix computations. In particular, auto-parallelization of irregular programs, which are organized around large, pointer-based data structures like graphs, has seemed intractable. The Galois project is taking a fresh look at autoparallelization. Rather than attempt to parallelize all programs no matter how obscurely they are written, we are designing programming abstractions that permit programmers to highlight opportunities for exploiting parallelism in sequential programs, and building a runtime system that uses these hints to execute the program in parallel. In this paper, we describe the design and implementation of a system based on these ideas. Experimental results for two real-world irregular applications, a Delaunay mesh refinement application and a graphics application that performs agglomerative clustering, demonstrate that this approach is promising.

Journal

Communications of the ACM – Association for Computing Machinery

Published: Sep 1, 2009

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