(PLDI 2020) Inductive Sequentialization of Asynchronous Programs
Asynchronous programs are notoriously difficult to reason about because they spawn computation tasks which take effect asynchronously in a non-deterministic way. Devising inductive invariants for such programs requires understanding and stating complex relationships between an unbounded number of computation tasks in arbitrarily long executions. In this paper, we introduce inductive sequentialization, a new proof rule that sidesteps this complexity via a sequential reduction, a sequential program that captures every behavior of the original program up to reordering of coarse-grained commutative actions. A sequential reduction of a concurrent program is easy to reason about since it corresponds to a simple execution of the program in an idealized synchronous environment, where processes act in a fixed order and at the same speed. We have implemented and integrated our proof rule in the CIVL verifier, allowing us to provably derive fine-grained implementations of asynchronous programs. We have successfully applied our proof rule to a diverse set of message-passing protocols, including leader election protocols, Two-Phase Commit, and Paxos.
Wed 15 JunDisplayed time zone: Pacific Time (US & Canada) change
15:30 - 16:50 | |||
15:30 20mTalk | (PLDI 2020) Inductive Sequentialization of Asynchronous Programs SIGPLAN Track Constantin Enea Ecole Polytechnique / LIX / CNRS, Thomas A. Henzinger IST Austria, Austria, Bernhard Kragl IST Austria, Suha Orhun Mutluergil IRIF, France / University Paris Diderot, France / CNRS, France, Shaz Qadeer Novi, USA | ||
15:50 20mTalk | (PLDI 2021) When Threads Meet Events: Efficient and Precise Static Race Detection with Origins SIGPLAN Track Bozhen Liu Texas A&M University, USA, Peiming Liu Texas A&M University, Yanze Li University of British Columbia, Chia-Che Tsai Texas A&M University, Dilma Da Silva Texas A&M, Jeff Huang Texas A&M University | ||
16:10 20mTalk | (POPL 2021) Optimal Prediction of Synchronization-Preserving Races SIGPLAN Track Umang Mathur National University of Singapore, Andreas Pavlogiannis Aarhus University, Mahesh Viswanathan University of Illinois at Urbana-Champaign | ||
16:30 20mTalk | (POPL 2022) Visibility Reasoning for Concurrent Snapshot Algorithms SIGPLAN Track Joakim Öhman IMDEA Software Institute; Universidad Politécnica de Madrid, Aleksandar Nanevski IMDEA Software Institute |