Google AI Professional Certificate - Learn AI Skills That Get You Hired
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Overview
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Watch a 22-minute conference presentation from POPL 2018 that introduces a novel dynamic partial-order reduction method for stateless model checking of concurrent programs. Learn about observation equivalence, a new data-centric approach that provides a coarser partitioning of program traces compared to traditional Mazurkiewicz equivalence. Explore how this method guarantees exploration of exactly one representative trace from each observation class in acyclic architectures while maintaining polynomial time complexity per class. Understand the experimental results showing significant improvements in both execution time and number of explored equivalence classes compared to existing Mazurkiewicz-based DPOR approaches. Presented by researchers from Masaryk University, IST Austria, Kena Labs, and IIT Bombay, this talk demonstrates how data-centric DPOR achieves optimal exploration under trace equivalence for both cyclic and acyclic architectures.
Syllabus
[POPL'18] Data-Centric Dynamic Partial Order Reduction
Taught by
ACM SIGPLAN