research-article

IFRit: interference-free regions for dynamic data-race detection

Authors:

Laura Effinger-Dean,

Hans-J. BoehmAuthors Info & Claims

ACM SIGPLAN Notices, Volume 47, Issue 10

Pages 467 - 484

https://doi.org/10.1145/2398857.2384650

Published: 19 October 2012 Publication History

Abstract

We propose a new algorithm for dynamic data-race detection. Our algorithm reports no false positives and runs on arbitrary C and C++ code. Unlike previous algorithms, we do not have to instrument every memory access or track a full happens-before relation. Our data-race detector, which we call IFRit, is based on a run-time abstraction called an interference-free region (IFR). An IFR is an interval of one thread's execution during which any write to a specific variable by a different thread is a data race. We insert instrumentation at compile time to monitor active IFRs at run-time. If the runtime observes overlapping IFRs for conflicting accesses to the same variable in two different threads, it reports a race. The static analysis aggregates information for multiple accesses to the same variable, avoiding the expense of having to instrument every memory access in the program.

We directly compare IFRit to FastTrack and ThreadSanitizer, two state-of-the-art fully-precise data-race detectors. We show that IFRit imposes a fraction of the overhead of these detectors. We show that for the PARSEC benchmarks, and several real-world applications, IFRit finds many of the races detected by a fully-precise detector. We also demonstrate that sampling can further reduce IFRit's performance overhead without completely forfeiting precision.

References

[1]

M. Abadi, C. Flanagan, and S. N. Freund. Types for safe locking: Static race detection for Java. ACM Transactions on Programming Languages and Systems (TOPLAS), 28(2):207--255, March 2006.

Digital Library

[2]

S. V. Adve and H.-J. Boehm. Memory models: A case for rethinking parallel languages and hardware. Communications of the ACM (CACM), 53(8):90--101, August 2010.

Digital Library

[3]

C. Bienia. Benchmarking Modern Multiprocessors. PhD thesis, Princeton University, January 2011.

Digital Library

[4]

H.-J. Boehm. How to miscompile programs with "benign" data races. In USENIX Workshop on Hot Topics in Parallelism (HotPar), 2011.

Digital Library

[5]

H.-J. Boehm and S. Adve. Foundations of the C++ con-currency memory model. In ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2008.

Digital Library

[6]

M. D. Bond, K. E. Coons, and K. S. McKinley. Pacer: proportional detection of data races. In ACM SIGPLAN Confer-ence on Programming Language Design and Implementation (PLDI), 2010.

Digital Library

[7]

L. Effinger-Dean, H.-J. Boehm, P. Joisha, and D. Chakrabarti. Extended sequential reasoning for data-race-free programs. In ACM SIGPLAN Workshop on Memory Systems Performance and Correctness (MSPC), 2011.

Digital Library

[8]

D. Engler and K. Ashcroft. RacerX: effective, static detection of race conditions and deadlocks. In ACM Symposium on Operating Systems Principles (SOSP), 2003.

Digital Library

[9]

J. Erickson, M. Musuvathi, S. Burckhardt, and K. Olynyk. Effective data-race detection for the kernel. In USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2010.

Digital Library

[10]

C. Flanagan and S. N. Freund. FastTrack: efficient and precise dynamic race detection. In ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2009.

Digital Library

[11]

ISO JTC1/SC22/WG14. ISO/IEC 9899:2011, Information technology "Programming languages" C. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=57853. Draft available at http://www.open-std.org/jtc1/sc22/wg14/www/docs/n1570.pdf.

[12]

ISO JTC1/SC22/WG21. ISO/IEC 14882:2011, Information technology -- Programming languages -- C++. http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=50372. Draft available at http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2012/n3337.pdf.

[13]

L. Lamport. Time, clocks, and the ordering of events in a distributed system. Communications of the ACM (CACM), 21 (7):558--565, July 1978.

Digital Library

[14]

C. Lattner and V. Adve. LLVM: A compilation framework for lifelong program analysis & transformation. In International Symposium on Code Generation and Optimization (CGO), 2004.

Digital Library

[15]

S. Lu, S. Park, E. Seo, and Y. Zhou. Learning from mistakes: a comprehensive study on real world concurrency bug characteristics. In International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2008.

Digital Library

[16]

B. Lucia, L. Ceze, K. Strauss, S. Qadeer, and H.-J. Boehm. Conflict exceptions: Simplifying concurrent language semantics with precise hardware exceptions for data-races. In ACM IEEE International Symposium on Computer Architecture (ISCA), 2010.

Digital Library

[17]

B. Lucia, B. P. Wood, and L. Ceze. Isolating and understand-ing concurrency errors using reconstructed execution fragments. In ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2011.

Digital Library

[18]

D. Marino, M. Musuvathi, and S. Narayanasamy. LiteRace: effective sampling for lightweight data-race detection. In ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2009.

Digital Library

[19]

F. Mattern. Virtual time and global states of distributed systems. In International Workshop on Parallel and Distributed Algorithms and Applications (PDAA), 1988.

[20]

M. Naik, A. Aiken, and J. Whaley. Effective static race detection for Java. In ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2006.

Digital Library

[21]

M. Olszewski, Q. Zhao, D. Koh, J. Ansel, and S. Ama-rasinghe. Aikido: accelerating shared data dynamic analyses. In International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2012.

Digital Library

[22]

E. Pozniansky and A. Schuster. MultiRace: Efficient on-the-fly data race detection in multithreaded C++ programs. Concurrency and Computation: Practice and Experience, 19 (3):327--340, March 2007.

Digital Library

[23]

D. Saff and M. D. Ernst. Reducing wasted development time via continuous testing. In IEEE International Symposium on Software Reliability Engineering (ISSRE), 2003.

Digital Library

[24]

S. Savage, M. Burrows, G. Nelson, P. Sobalvarro, and T. An-derson. Eraser: a dynamic data race detector for multithreaded programs. ACM Transactions on Computer Systems (TOCS), 15(4):391--411, November 1997.

Digital Library

[25]

K. Serebryany and T. Iskhodzhanov. ThreadSanitizer--data race detection in practice. In Workshop on Binary Instrumentation and Applications, 2009.

Digital Library

[26]

J. Sevc1k and D. Aspinall. On validity of program transforma-tions in the Java memory model. In European Conference on Object-Oriented Programming (ECOOP), 2008.

Digital Library

[27]

J. Yu and S. Narayanasamy. A case for an interleaving con-strained shared-memory multiprocessor. In ACM IEEE International Symposium on Computer Architecture (ISCA), 2009.

Digital Library

[28]

Y. Yu, T. Rodeheffer, and W. Chen. RaceTrack: efficient detection of data race conditions via adaptive tracking. In ACM Symposium on Operating Systems Principles (SOSP), 2005.

Digital Library

Cited By

Zhu SGuo YZhang LCai YGrundy JPollock LPenta M(2023)Tolerate Control-Flow Changes for Sound Data Race PredictionProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00118(1342-1354)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE48619.2023.00118
Jain RPurandare RSharma S(2022)BiRD: Race Detection in Software Binaries under Relaxed Memory ModelsACM Transactions on Software Engineering and Methodology10.1145/3498538Online publication date: 31-Jan-2022
https://doi.org/10.1145/3498538
Cai YYun HWang JQiao LPalsberg JSpinellis DGousios GChechik MDi Penta M(2021)Sound and efficient concurrency bug predictionProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468549(255-267)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3468549
Show More Cited By

Recommendations

IFRit: interference-free regions for dynamic data-race detection
OOPSLA '12: Proceedings of the ACM international conference on Object oriented programming systems languages and applications

We propose a new algorithm for dynamic data-race detection. Our algorithm reports no false positives and runs on arbitrary C and C++ code. Unlike previous algorithms, we do not have to instrument every memory access or track a full happens-before ...
Low-level detection of language-level data races with LARD
ASPLOS '14

Researchers have proposed always-on data-race exceptions as a way to avoid the ill effects of data races, but slow performance of accurate dynamic data-race detection remains a barrier to the adoption of always-on data-race exceptions. Proposals for ...
Low-level detection of language-level data races with LARD
ASPLOS '14

Researchers have proposed always-on data-race exceptions as a way to avoid the ill effects of data races, but slow performance of accurate dynamic data-race detection remains a barrier to the adoption of always-on data-race exceptions. Proposals for ...

Comments

Information & Contributors

Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 47, Issue 10

OOPSLA '12

October 2012

1011 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2398857

Issue’s Table of Contents

OOPSLA '12: Proceedings of the ACM international conference on Object oriented programming systems languages and applications
October 2012
1052 pages
ISBN:9781450315616
DOI:10.1145/2384616
General Chair:
Gary T. Leavens
University of Central Florida
,
Program Chair:
Matthew B. Dwyer
University of Nebraska - Lincoln

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2012

Published in SIGPLAN Volume 47, Issue 10

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

91
Total Citations
View Citations
433
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)1

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhu SGuo YZhang LCai YGrundy JPollock LPenta M(2023)Tolerate Control-Flow Changes for Sound Data Race PredictionProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00118(1342-1354)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE48619.2023.00118
Jain RPurandare RSharma S(2022)BiRD: Race Detection in Software Binaries under Relaxed Memory ModelsACM Transactions on Software Engineering and Methodology10.1145/3498538Online publication date: 31-Jan-2022
https://doi.org/10.1145/3498538
Cai YYun HWang JQiao LPalsberg JSpinellis DGousios GChechik MDi Penta M(2021)Sound and efficient concurrency bug predictionProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468549(255-267)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3468549
Genç KRoemer JXu YBond M(2019)Dependence-aware, unbounded sound predictive race detectionProceedings of the ACM on Programming Languages10.1145/33606053:OOPSLA(1-30)Online publication date: 10-Oct-2019
https://dl.acm.org/doi/10.1145/3360605
Wilcox JFlanagan CFreund S(2018)VerifiedFTACM SIGPLAN Notices10.1145/3200691.317851453:1(354-367)Online publication date: 10-Feb-2018
https://dl.acm.org/doi/10.1145/3200691.3178514
Yu MLee JBae D(2018)AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking PatternsInternational Journal of Parallel Programming10.1007/s10766-018-0579-547:5-6(805-837)Online publication date: 4-Jun-2018
https://doi.org/10.1007/s10766-018-0579-5
KANG P(2017)Software Analysis Techniques for Detecting Data RaceIEICE Transactions on Information and Systems10.1587/transinf.2017EDR0004E100.D:11(2674-2682)Online publication date: 2017
https://doi.org/10.1587/transinf.2017EDR0004
Yu TPradel MZeller ARoychoudhury A(2016)SyncProf: detecting, localizing, and optimizing synchronization bottlenecksProceedings of the 25th International Symposium on Software Testing and Analysis10.1145/2931037.2931070(389-400)Online publication date: 18-Jul-2016
https://dl.acm.org/doi/10.1145/2931037.2931070
Sengupta ABiswas SZhang MBond MKulkarni MOzturk OEbcioglu KDwarkadas S(2015)Hybrid Static–Dynamic Analysis for Statically Bounded Region SerializabilityProceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/2694344.2694379(561-575)Online publication date: 14-Mar-2015
https://dl.acm.org/doi/10.1145/2694344.2694379
Flanagan CFreund S(2013)RedCardProceedings of the 27th European conference on Object-Oriented Programming10.1007/978-3-642-39038-8_11(255-280)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1007/978-3-642-39038-8_11
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents