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Lerna

Lerna We present Lerna, an end-to-end tool that automatically and transparently detects and extracts parallelism from data-dependent sequential loops. Lerna uses speculation combined with a set of techniques including code profiling, dependency analysis, instrumentation, and adaptive execution. Speculation is needed to avoid conservative actions and detect actual conflicts. Lerna targets applications that are hard-to-parallelize due to data dependency. Our experimental study involves the parallelization of 13 applications with data dependencies. Results on a 24-core machine show an average of 2.7 speedup for micro-benchmarks and 2.5 for the macro-benchmarks. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Storage (TOS) Association for Computing Machinery

Lerna

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2019 ACM
ISSN
1553-3077
eISSN
1553-3093
DOI
10.1145/3310368
Publisher site
See Article on Publisher Site

Abstract

We present Lerna, an end-to-end tool that automatically and transparently detects and extracts parallelism from data-dependent sequential loops. Lerna uses speculation combined with a set of techniques including code profiling, dependency analysis, instrumentation, and adaptive execution. Speculation is needed to avoid conservative actions and detect actual conflicts. Lerna targets applications that are hard-to-parallelize due to data dependency. Our experimental study involves the parallelization of 13 applications with data dependencies. Results on a 24-core machine show an average of 2.7 speedup for micro-benchmarks and 2.5 for the macro-benchmarks.

Journal

ACM Transactions on Storage (TOS) – Association for Computing Machinery

Published: Mar 22, 2019

Keywords: Code parallelization

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