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A Load-Buffer Semantics for Total Store Ordering
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
(English)In: Logical Methods in Computer Science, ISSN 1860-5974, E-ISSN 1860-5974Article in journal, Editorial material (Refereed) Accepted
Abstract [en]

We address the problem of verifying safety properties of concurrent programs running over the Total Store Order (TSO) memory model. Known decision procedures for this model are based on complex encodings of store buffers as lossy channels. These procedures assume that the number of processes is fixed. However, it is important in general to prove the correctness of a system/algorithm in a parametric way with an arbitrarily large number of processes. In this paper, we introduce an alternative (yet equivalent) semantics to the classical one for the TSO semantics that is more amenable to efficient algorithmic verification and for the extension to parametric verification. For that, we adopt a dual view where load buffers are used instead of store buffers. The flow of information is now from the memory to load buffers. We show that this new semantics allows (1) to simplify drastically the safety analysis under TSO, (2) to obtain a spectacular gain in efficiency and scalability compared to existing procedures, and (3) to extend easily the decision procedure to the parametric case, which allows obtaining a new decidability result, and more importantly, a verification algorithm that is more general and more efficient in practice than the one for bounded instances.

Keyword [en]
Total Store Order, Weak Memory Models, Reachability Problem, Parameterized Systems, Well-quasi-ordering
National Category
Computer and Information Sciences
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:uu:diva-337278OAI: oai:DiVA.org:uu-337278DiVA: diva2:1168754
Projects
UPMARC
Available from: 2017-12-21 Created: 2017-12-21 Last updated: 2018-01-13

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