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Talks & Seminars
Title: Dynamic Race Prediction in Linear Time
Mr. Umang Mathur, University of Illinois, Urbana Champaign
Date & Time: September 5, 2018 15:00
Venue: Conference Room, C Block, 01st Floor, Department of Computer Science and Engineering, Kanwal Rekhi (KReSIT) Building
Abstract:
Writing reliable concurrent software remains a huge challenge for today's programmers. Programmers rarely reason about their code by explicitly considering different possible inter-leavings of its execution. We consider the problem of detecting data races from individual executions in a sound manner. The classical approach to solving this problem has been to use Lamport's happens-before (HB) relation. Until now HB remains the only approach that runs in linear time. Previous efforts in improving over HB such as causally-precedes (CP) and maximal causal models fall short due to the fact that they are not implementable efficiently and hence have to compromise on their race detecting ability by limiting their techniques to bounded sized fragments of the execution. We present a new relation weak-causally-precedes (WCP) that is provably better than CP in terms of being able to detect more races, while still remaining sound. Moreover it admits a linear time algorithm which works on the entire execution without having to fragment it.
Speaker Profile:
Umang Mathur is a 4th year Ph. D. candidate at University of Illinois, Urbana Champaign. He graduated from IIT Bombay in 2014. His research interests lie in formal methods and its applications to programming languages and software engineering. His research currently focuses on dynamic analysis techniques to detect bugs in concurrent programs.
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