Eliminating Redundant Computation and Exposing Parallelism through Data-Triggered Threads

Authors:
Hung-Wei Tseng;Dean M. Tullsen
Affiliations:
University of California, San Diego;University of California, San Diego
Venue:
IEEE Micro
Year:
2012

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Software data-triggered threads

Proceedings of the ACM international conference on Object oriented programming systems languages and applications

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Abstract

Unlike threads in parallel programs created by conventional programming, data-triggered threads are initiated when a memory value is changed. By expressing computation through these threads, computation is executed only when the data changes and is skipped whenever the data does not change. The authors' model achieves performance speedups of up to 5.9x, averaging 45.6 percent, with SPEC2000 benchmarks.