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How much parallelism is there in irregular applications?
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Principles and Practice of Parallel Programming archive
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming table of contents
Raleigh, NC, USA
SESSION: Parallel applications table of contents
Pages 3-14  
Year of Publication: 2009
ISBN:978-1-60558-397-6
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Authors
Milind Kulkarni  The University of Texas at Austin, Austin, TX, USA
Martin Burtscher  The University of Texas at Austin, Austin, TX, USA
Rajeshkar Inkulu  The University of Texas at Austin, Austin, TX, USA
Keshav Pingali  The University of Texas at Austin, Austin, TX, USA
Calin Casçaval  IBM Research, Yorktown Heights, NY, USA
Sponsors
ACM: Association for Computing Machinery
SIGPLAN: ACM Special Interest Group on Programming Languages
Publisher
ACM  New York, NY, USA
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ABSTRACT

Irregular programs are programs organized around pointer-based data structures such as trees and graphs. Recent investigations by the Galois project have shown that many irregular programs have a generalized form of data-parallelism called amorphous data-parallelism. However, in many programs, amorphous data-parallelism cannot be uncovered using static techniques, and its exploitation requires runtime strategies such as optimistic parallel execution. This raises a natural question: how much amorphous data-parallelism actually exists in irregular programs?

In this paper, we describe the design and implementation of a tool called ParaMeter that produces parallelism profiles for irregular programs. Parallelism profiles are an abstract measure of the amount of amorphous data-parallelism at different points in the execution of an algorithm, independent of implementation-dependent details such as the number of cores, cache sizes, load-balancing, etc. ParaMeter can also generate constrained parallelism profiles for a fixed number of cores. We show parallelism profiles for seven irregular applications, and explain how these profiles provide insight into the behavior of these applications.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Milind Kulkarni: colleagues
Martin Burtscher: colleagues
Rajeshkar Inkulu: colleagues
Keshav Pingali: colleagues
Calin Casçaval: colleagues