License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ECOOP.2017.15
URN: urn:nbn:de:0030-drops-72722
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7272/
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Huang, Jeff ; Rajagopalan, Arun K.

What'’s the Optimal Performance of Precise Dynamic Race Detection? –A Redundancy Perspective

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Abstract

In a precise data race detector, a race is detected only if the execution exhibits a real race. In such tools, every memory access from each thread is typically checked by a happens-before algorithm. What’s the optimal runtime performance of such tools? In this paper, we identify that a significant percentage of memory access checks in real-world program executions are often redundant: removing these checks affects neither the precision nor the capability of race detection. We show that if all such redundant checks were eliminated with no cost, the optimal performance of a state-of-the-art dynamic race detector, FastTrack, could be improved by 90%, reducing its runtime overhead from 68X to 7X on a collection of CPU intensive benchmarks. We further develop a purely dynamic technique, ReX, that efficiently filters out redundant checks and apply it to FastTrack. With ReX, the runtime performance of FastTrack is improved by 31% on average.

BibTeX - Entry

@InProceedings{huang_et_al:LIPIcs:2017:7272,
  author =	{Jeff Huang and Arun K. Rajagopalan},
  title =	{{What'’s the Optimal Performance of Precise Dynamic Race Detectionl –A Redundancy Perspective}},
  booktitle =	{31st European Conference on Object-Oriented Programming (ECOOP 2017)},
  pages =	{15:1--15:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-035-4},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{74},
  editor =	{Peter M{\"u}ller},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7272},
  URN =		{urn:nbn:de:0030-drops-72722},
  doi =		{10.4230/LIPIcs.ECOOP.2017.15},
  annote =	{Keywords: Data Race Detection, Dynamic Analysis, Concurrency, Redundancy}
}

Keywords: Data Race Detection, Dynamic Analysis, Concurrency, Redundancy
Collection: 31st European Conference on Object-Oriented Programming (ECOOP 2017)
Issue Date: 2017
Date of publication: 16.06.2017

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