University of Hertfordshire

Dynamic Deadlock Verification for General Barrier Synchronisation

Research output: Contribution to journalArticlepeer-review

  • Tiago Cogumbreiro
  • Raymond Hu
  • Francisco Martins
  • Nobuko Yoshida
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Original languageEnglish
Article number1
Pages (from-to)1-38
Number of pages38
JournalACM Transactions on Programming Languages and Systems (TOPLAS)
Early online date1 Dec 2018
Publication statusPublished - 1 Mar 2019


We present Armus, a verification tool for dynamically detecting or avoiding barrier deadlocks. The core design of Armus is based on phasers, a generalisation of barriers that supports split-phase synchronisation, dynamic
membership, and optional-waits. This allows Armus to handle the key barrier synchronisation patterns found in modern languages and libraries. We implement Armus for X10 and Java, giving the first sound and complete barrier deadlock verification tools in these settings.

Armus introduces a novel event-based graph model of barrier concurrency constraints that distinguishes task-event and event-task dependencies. Decoupling these two kinds of dependencies facilitates the verification of distributed barriers with dynamic membership, a challenging feature of X10. Further, our base graph representation can be dynamically switched between a task-to-task model, Wait-for Graph (WFG), and an event-to-event model, State
Graph (SG), to improve the scalability of the analysis.

Formally, we show that the verification is sound and complete with respect to the occurrence of deadlock in our core phaser language; and that switching graph representations preserves the soundness and completeness properties.
These results are machine checked with the Coq proof assistant. Practically, we evaluate the runtime overhead of our implementations using three benchmark suites in local and distributed scenarios. Regarding deadlock detection,
distributed scenarios show negligible overheads and local scenarios show overheads below 1.15×. Deadlock avoidance is more demanding, and highlights the potential gains from dynamic graph selection. In one benchmark
scenario, the runtime overheads vary from: 1.8× for dynamic selection, 2.6× for SG-static selection, and 5.9× for WFG-static selection.

ID: 16788533