Local Data Race Freedom with Non-Multi-Copy Atomicity Local Data Race Freedom with Non-Multi-Copy Atomicity
Tatsuya Abe. Local Data Race Freedom with Non-Multi-Copy Atomicity. In Proceedings of SPIN, volume 10869 of Lecture Notes in Computer Science, pages 196–215. Malaga, June 2018.
Data race freedom ensures the sequentially consistent behaviors of concurrent programs under relaxed memory consistency models (MCMs), and reduces the state explosion problem for software model checking with MCMs. However, data race freedom is too strong to include all interesting programs. In this paper, we define small-step operational semantics for relaxed MCMs, define an observable equivalence using the notion of bisimulation, and propose the property of local data race freedom (LDRF), which requires a kind of race freedom locally instead of globally. LDRF includes some interesting programs, such as the independent reads independent writes program, which is well known to exhibit curious behaviors under non-multi-copy atomic MCMs, and some concurrent copying garbage collection algorithms. In this paper, we introduce an optimization method called memory sharing for model checking of LDRF programs, and show that memory sharing optimization mitigates state explosion problems with non-multi-copy atomic MCMs through experiments.