Optimistic Prediction of Synchronization-Reversal Data Races

TL;DR

This paper introduces a new class of data races called optimistic synchronization-reversal races, along with an efficient algorithm OSR that detects these races in large concurrent programs, improving over prior methods.

Contribution

The paper defines optimistic sync-reversal races, presents a quadratic-time sound detection algorithm OSR, and proves its optimality, enabling scalable detection of complex data races.

Findings

OSR detects more data races than existing tools.

OSR scales well to large execution traces.

The algorithm runs in quadratic time and is proven optimal.

Abstract

Dynamic data race detection has emerged as a key technique for ensuring reliability of concurrent software in practice. However, dynamic approaches can often miss data races owing to nondeterminism in the thread scheduler. Predictive race detection techniques cater to this shortcoming by inferring alternate executions that may expose data races without re-executing the underlying program. More formally, the dynamic data race prediction problem asks, given a trace \sigma of an execution of a concurrent program, can \sigma be correctly reordered to expose a data race? Existing state-of-the art techniques for data race prediction either do not scale to executions arising from real world concurrent software, or only expose a limited class of data races, such as those that can be exposed without reversing the order of synchronization operations. In general, exposing data races by reasoning about synchronization reversals is an intractable problem. In this work, we identify a class of data races, called Optimistic Sync(hronization)-Reversal races that can be detected in a tractable manner and often include non-trivial data races that cannot be exposed by prior tractable techniques. We also propose a sound algorithm OSR for detecting all optimistic sync-reversal data races in overall quadratic time, and show that the algorithm is optimal by establishing a matching lower bound. Our experiments demonstrate the effectiveness of OSR on our extensive suite of benchmarks, OSR reports the largest number of data races, and scales well to large execution traces.