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CC 2017
Sun 5 - Mon 6 February 2017 Austin, Texas, United States
Sun 5 Feb 2017 10:55 - 11:20 at 404 - Concurrency & Parallelism Chair(s): Sebastian Hack

To 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 soundness \emph{or} precision incur significant limitations.

This work addresses the need for soundness (no missed races) and precision (no false races) by introducing novel, efficient production-time analyses that address each need separately. (1) \emph{Precise} data race detection is useful for developers, who want to fix bugs but loathe false positives. We introduce a precise analysis called \emph{RaceChaser} that provides low, bounded run-time overhead. (2) \emph{Sound} race detection benefits analyses and tools whose correctness relies on knowledge of \emph{all} potential data races. We present a sound, efficient approach called \emph{Caper} that combines static and dynamic analysis to catch all data races in observed runs. RaceChaser and Caper are useful not only on their own; we introduce a framework that combines these analyses, using Caper as a sound filter for precise data race detection by RaceChaser.

Our evaluation shows that RaceChaser and Caper are efficient and effective, and compare favorably with existing state-of-the-art approaches. These results suggest that RaceChaser and Caper enable practical data race detection that is precise and sound, respectively, ultimately leading to more reliable software systems.

Sun 5 Feb
Times are displayed in time zone: Saskatchewan, Central America change

10:30 - 12:10: Concurrency & ParallelismResearch Papers at 404
Chair(s): Sebastian HackSaarland University
10:30 - 10:55
Talk
Partially Redundant Fence Elimination for x86, ARM, and Power Processors
Research Papers
Robin MorissetENS, France, Francesco Zappa NardelliInria, France
DOI
10:55 - 11:20
Talk
Lightweight Data Race Detection for Production Runs
Research Papers
Swarnendu BiswasUniversity of Texas at Austin, Man CaoOhio State University, Minjia ZhangOhio State University, Michael D. BondOhio State University, Benjamin P. WoodWellesley College, USA
DOI
11:20 - 11:45
Talk
Optimized Two-Level Parallelization for GPU Accelerators using the Polyhedral Model
Research Papers
Jun ShirakoRice University, USA, Akihiro HayashiRice University, USA, Vivek SarkarRice University, USA
DOI
11:45 - 12:10
Talk
Optimization Space Pruning without Regrets
Research Papers
DOI