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Documents authored by Huang, Jeff

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DOI: 10.4230/LIPIcs.ECOOP.2017.15
What'’s the Optimal Performance of Precise Dynamic Race Detection? –A Redundancy Perspective

Authors: Jeff Huang and Arun K. Rajagopalan

Published in: LIPIcs, Volume 74, 31st European Conference on Object-Oriented Programming (ECOOP 2017)

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.
Cite as

Jeff Huang and Arun K. Rajagopalan. What'’s the Optimal Performance of Precise Dynamic Race Detection? –A Redundancy Perspective. In 31st European Conference on Object-Oriented Programming (ECOOP 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 74, pp. 15:1-15:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{huang_et_al:LIPIcs.ECOOP.2017.15,
  author =	{Huang, Jeff and Rajagopalan, Arun K.},
  title =	{{What'’s the Optimal Performance of Precise Dynamic Race Detection? –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 =	{M\"{u}ller, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2017.15},
  URN =		{urn:nbn:de:0030-drops-72722},
  doi =		{10.4230/LIPIcs.ECOOP.2017.15},
  annote =	{Keywords: Data Race Detection, Dynamic Analysis, Concurrency, Redundancy}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2017.16
Speeding Up Maximal Causality Reduction with Static Dependency Analysis

Authors: Shiyou Huang and Jeff Huang

Published in: LIPIcs, Volume 74, 31st European Conference on Object-Oriented Programming (ECOOP 2017)

Abstract
Stateless Model Checking (SMC) offers a powerful approach to verifying multithreaded programs but suffers from the state-space explosion problem caused by the huge thread interleaving space. The pioneering reduction technique Partial Order Reduction (POR) mitigates this problem by pruning equivalent interleavings from the state space. However, limited by the happens-before relation, POR still explores redundant executions. The recent advance, Maximal Causality Reduction (MCR), shows a promising performance improvement over the existing reduction techniques, but it has to construct complicated constraints to ensure the feasibility of the derived execution due to the lack of dependency information. In this work, we present a new technique, which extends MCR with static analysis to reduce the size of the constraints, thus speeding up the exploration of the state space. We also address the redundancy problem caused by the use of static analysis. We capture the dependency between a read and a later event e in the trace from the system dependency graph and identify those reads that e is not control dependent on. Our approach then ignores the constraints over such reads to reduce the complexity of the constraints. The experimental results show that compared to MCR, the number of the constraints and the solving time by our approach are averagely reduced by 31.6% and 27.8%, respectively.
Cite as

Shiyou Huang and Jeff Huang. Speeding Up Maximal Causality Reduction with Static Dependency Analysis. In 31st European Conference on Object-Oriented Programming (ECOOP 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 74, pp. 16:1-16:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{huang_et_al:LIPIcs.ECOOP.2017.16,
  author =	{Huang, Shiyou and Huang, Jeff},
  title =	{{Speeding Up Maximal Causality Reduction with Static Dependency Analysis}},
  booktitle =	{31st European Conference on Object-Oriented Programming (ECOOP 2017)},
  pages =	{16:1--16:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-035-4},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{74},
  editor =	{M\"{u}ller, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2017.16},
  URN =		{urn:nbn:de:0030-drops-72523},
  doi =		{10.4230/LIPIcs.ECOOP.2017.16},
  annote =	{Keywords: Model Checking, Dynamic Analysis, Program Dependency Analysis}
}
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