Lerna-Reference-Cited by-同舟云学术

Lerna

Published:2019-04-20 Issue:1 Volume:15 Page:1-24
ISSN:1553-3077
Container-title:ACM Transactions on Storage
language:en
Short-container-title:ACM Trans. Storage

Author:

Saad Mohamed M.¹,Palmieri Roberto²,Ravindran Binoy³

Affiliation:

1. Alexandria University, Egypt

2. Lehigh University, Bethlehem, PA

3. Virginia Tech, Blacksburg, VA

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.

Funder

Air Force Office of Scientific Research

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture

Link

https://dl.acm.org/doi/pdf/10.1145/3310368

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