Tianwei Sheng
ABSTRACT
Concurrency bugs, particularly data races, are notoriously difficult to debug and are a significant source of unreliability in multithreaded applications. Many tools to catch data races rely on program instrumentation to obtain memory instruction traces. Unfortunately, this instrumentation introduces significant runtime overhead, is extremely invasive, or has a limited domain of applicability making these tools unsuitable for many production systems. Consequently, these tools are typically used during application testing where many data races go undetected.
This paper proposes RACEZ , a novel race detection mechanism which uses a sampled memory trace collected by the hardware performance monitoring unit rather than invasive instrumentation. The approach introduces only a modest overhead making it usable in production environments. We validate RACEZ using two open source server applications and the PARSEC benchmarks. Our experiments show that RACEZ catches a set of known bugs with reasonable probability while introducing only 2.8% runtime slow down on average.
- Concurrency bugs collection. http://www.eecs.umich.edu/~jieyu/bugs.html.Google Scholar
- Mao - an extensible micro-architectural optimizer. http://code.google.com/p/mao/.Google Scholar
- perfmon2: the hardware-based performance monitoring interface for linux. http://perfmon2.sourceforge.net/.Google Scholar
- M. Arnold, M. T. Vechev, and E. Yahav. QVM: an efficient runtime for detecting defects in deployed systems. In OOPSLA, pages 143--162. ACM, 2008. Google ScholarDigital Library
- M. D. Bond, K. E. Coons, and K. S. McKinley. Pacer: Proportional detection of data races. In PLDI, 2010. Google ScholarDigital Library
- S. Burckhardt, P. Kothari, M. Musuvathi, and S. Nagarakatte. A randomized scheduler with probabilistic guarantees of finding bugs. In ASPLOS, pages 167--178, 2010. Google ScholarDigital Library
- D. Chen, N. Vachharajani, R. Hundt, S. wei Liao, V. Ramasamy, P. Yuan, W. Chen, and W. Zheng. Taming hardware event samples for fdo compilation. In Code Generation and Optimization(CGO), April 24-28, 2010. Google ScholarDigital Library
- J. Dean, J. E. Hicks, C. A. Waldspurger, W. E. Weihl, and G. Z. Chrysos. ProfileMe: Hardware support for instruction-level profiling on out-of-order processors. In MICRO, pages 292--302, 1997. Google ScholarDigital Library
- D. Engler and K. Ashcraft. RacerX: effective, static detection of race conditions and deadlocks. In Proceedings of the nineteenth ACM symposium on Operating systems principles, pages 237--252, New York, 2003. Google ScholarDigital Library
- C. Flanagan and S. N. Freund. Fasttrack: efficient and precise dynamic race detection. In PLDI, pages 121--133, 2009. Google ScholarDigital Library
- H. Jula, D. M. Tralamazza, C. Zamfir, and G. Candea. Deadlock immunity: Enabling systems to defend against deadlocks. In OSDI, pages 295--308, 2008. Google ScholarDigital Library
- S. Lu, S. Park, E. Seo, and Y. Zhou. Learning from mistakes: a comprehensive study on real world concurrency bug characteristics. In ASPLOS, pages 329--339, 2008. Google ScholarDigital Library
- S. Lu, J. Tucek, F. Qin, and Y. Zhou. Avio: detecting atomicity violations via access interleaving invariants. In ASPLOS, pages 37--48, 2006. Google ScholarDigital Library
- D. Marino, M. Musuvathi, and S. Narayanasamy. Literace: effective sampling for lightweight data-race detection. In PLDI, pages 134--143, 2009. Google ScholarDigital Library
- M. Musuvathi, S. Qadeer, T. Ball, G. Basler, P. A. Nainar, and I. Neamtiu. Finding and reproducing heisenbugs in concurrent programs. In OSDI, pages 267--280, 2008. Google ScholarDigital Library
- A. Muzahid, D. S. Gracia, S. Qi, and J. Torrellas. Sigrace: signature-based data race detection. In S. W. Keckler and L. A. Barroso, editors, ISCA, pages 337--348. ACM, 2009. Google ScholarDigital Library
- R. O'Callahan and J.-D. Choi. Hybrid dynamic data race detection. In PPOPP, pages 167--178, 2003. Google ScholarDigital Library
- S. Park, S. Lu, and Y. Zhou. Ctrigger: exposing atomicity violation bugs from their hiding places. In ASPLOS, pages 25--36, 2009. Google ScholarDigital Library
- 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, 2007. Google ScholarDigital Library
- F. Qin, J. Tucek, J. Sundaresan, and Y. Zhou. Rx: treating bugs as allergies - a safe method to survive software failures. In SOSP, pages 235--248, 2005. Google ScholarDigital Library
- P. Sack, B. E. Bliss, Z. Ma, P. Petersen, and J. Torrellas. Accurate and efficient filtering for the intel thread checker race detector. In ASID, pages 34--41, 2006. Google ScholarDigital Library
- S. Savage, M. Burrows, G. Nelson, P. Sobalvarro, and T. E. Anderson. Eraser: A dynamic data race detector for multithreaded programs. ACM Trans. Comput. Syst, 15(4):391--411, 1997. Google ScholarDigital Library
- SecurityFocus. Software bug constributed to blackout. http://www.securityfocus.com/news/8016.Google Scholar
- K. Sen. Race directed random testing of concurrent programs. In PLDI, pages 11--21, 2008. Google ScholarDigital Library
- K. Serebryany and T. Iskhodzhanov. Threadsanitizer - data race detection in practice. In WBIA09, December 12, 2009. Google ScholarDigital Library
- A. Shye, M. Iyer, V. J. Reddi, and D. A. Connors. Code coverage testing using hardware performance monitoring support. In AADEBUG, pages 159--163, 2005. Google ScholarDigital Library
- J. W. Voung, R. Jhala, and S. Lerner. Relay: static race detection on millions of lines of code. In ESEC/SIGSOFT FSE, pages 205--214, 2007. Google ScholarDigital Library
- J. Wu, H. Cui, and J. Yang. Bypassing races in live applications with execution filters. In Proceedings of the Ninth Symposium on Operating Systems Design and Implementation (OSDI '10), 2010. Google ScholarDigital Library
- W. Zhang, C. Sun, and S. Lu. Conmem: detecting severe concurrency bugs through an effect-oriented approach. In ASPLOS, pages 179--192, 2010. Google ScholarDigital Library
- P. Zhou, R. Teodorescu, and Y. Zhou. Hard: Hardware-assisted lockset-based race detection. In HPCA, pages 121--132, 2007. Google ScholarDigital Library
- P. Zijlstra. Per-thread self-monitoring official linux support patch. http://lkml.org/lkml/2009/8/4/128.Google Scholar
Index Terms
- RACEZ: a lightweight and non-invasive race detection tool for production applications
Recommendations
PACER: proportional detection of data races
PLDI '10Data races indicate serious concurrency bugs such as order, atomicity, and sequential consistency violations. Races are difficult to find and fix, often manifesting only after deployment. The frequency and unpredictability of these bugs will only ...
PACER: proportional detection of data races
PLDI '10: Proceedings of the 31st ACM SIGPLAN Conference on Programming Language Design and ImplementationData races indicate serious concurrency bugs such as order, atomicity, and sequential consistency violations. Races are difficult to find and fix, often manifesting only after deployment. The frequency and unpredictability of these bugs will only ...
Lightweight data race detection for production runs
CC 2017: Proceedings of the 26th International Conference on Compiler ConstructionTo detect data races that harm production systems, program analysis must target production runs. However, sound and precise data race detection adds too much run-time overhead for use in production systems. Even existing approaches that provide ...
Comments