Data races are a real problem for parallel software, yet hard to detect.
Sound predictive analysis observes a program execution and detects data races
that exist in some other, unobserved execution.
However, existing predictive analyses miss races because they do not
scale to full program executions or do not precisely incorporate data and control dependence.
This paper introduces two novel, sound predictive approaches that incorporate data and control dependence and handle full program executions.
An evaluation using real, large Java programs shows that these approaches
detect more data races than the closest related approaches,
thus advancing the state of the art in sound predictive race detection.
Fri 25 Oct Times are displayed in time zone: Beirut change
|11:00 - 11:22|
|Efficient Lock-Free Durable Sets|
Yoav ZurielTechnion - Israel, Michal FriedmanTechnion - Israel, Gali SheffiTechnion - Israel, Nachshon CohenAmazon, Erez PetrankTechnion - IsraelDOI
|11:22 - 11:45|
|Weak Persistency Semantics from the Ground Up: Formalising the Persistency Semantics of ARMv8 and Transactional Models|
Azalea RaadMPI-SWS, Germany, John WickersonImperial College London, Viktor VafeiadisMPI-SWS, GermanyDOI
|11:45 - 12:07|
|Verifying Safety and Accuracy of Approximate Parallel Programs via Canonical Sequentialization|
Vimuth FernandoUniversity of Illinois at Urbana-Champaign, Keyur JoshiUniversity of Illinois at Urbana-Champaign, Sasa MisailovicUniversity of Illinois at Urbana-ChampaignDOI
|12:07 - 12:30|
|Dependence-Aware, Unbounded Sound Predictive Race Detection|
Kaan GençOhio State University, Jake RoemerOhio State University, Yufan XuOhio State University, Michael D. BondOhio State UniversityDOI Pre-print