2017/04/16 by Dileep Kini, Umang Mathur, Mahesh Viswanathan · 2 citations
Computer Science · Mathematics · #Algorithm #Bounded function #Code (set theory) #Computer science #Data mining #Limiting #Logic, programming, and type systems #Mathematics #Parallel Computing and Optimization Techniques #Parallel computing #Programming language #Race (biology) #Relation (database) #Software Testing and Debugging Techniques #Theoretical computer science #cs.PL #cs.SE
paper · pdf · doi:10.1145/3140587.3062374
22 pages, 8 figures, 1 algorithm, 1 table
arxiv created 2017/04/16 · openalex publication_date 2017/06/14 · arxiv updated 2017/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.