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COMPASS: A Community-driven Parallelization Advisor for Sequential Software

Lakshminarasimhan Sethumadhavan; Gail E. Kaiser

Title:
COMPASS: A Community-driven Parallelization Advisor for Sequential Software
Author(s):
Sethumadhavan, Lakshminarasimhan
Kaiser, Gail E.
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-021-09
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
The widespread adoption of multicores has renewed the emphasis on the use of parallelism to improve performance. The present and growing diversity in hardware architectures and software environments, however, continues to pose difficulties in the effective use of parallelism thus delaying a quick and smooth transition to the concurrency era. In this paper, we describe the research being conducted at Columbia University on a system called COMPASS that aims to simplify this transition by providing advice to programmers while they reengineer their code for parallelism. The advice proffered to the programmer is based on the wisdom collected from programmers who have already parallelized some similar code. The utility of COMPASS rests, not only on its ability to collect the wisdom unintrusively but also on its ability to automatically seek, find and synthesize this wisdom into advice that is tailored to the task at hand, i.e., the code the user is considering parallelizing and the environment in which the optimized program is planned to execute. COMPASS provides a platform and an extensible framework for sharing human expertise about code parallelization — widely, and on diverse hardware and software. By leveraging the "wisdom of crowds" model, which has been conjectured to scale exponentially and which has successfully worked for wikis, COMPASS aims to enable rapid propagation of knowledge about code parallelization in the context of the actual parallelization reengineering, and thus continue to extend the benefits of Moore's law scaling to science and society.
Subject(s):
Computer science
Item views:
101
Metadata:
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