Efficient Dynamic Algorithms to Predict Short Races

TL;DR

This paper develops efficient algorithms for detecting short races in concurrent traces, improving speed and memory efficiency over existing methods while increasing race detection capabilities.

Contribution

Introduces a monitoring framework for short-race prediction and provides novel algorithms for happens-before and sync-preserving races with improved efficiency.

Findings

Algorithms run faster than existing methods.

Use significantly less memory.

Detect more races under the same resource constraints.

Abstract

We introduce and study the problem of detecting short races in an observed trace. Specifically, for a race type $R$, given a trace $\sigma$ and window size $w$, the task is to determine whether there exists an $R$-race $(e_1, e_2)$ in $\sigma$ such that the subtrace starting with $e_1$ and ending with $e_2$ contains at most $w$ events. We present a monitoring framework for short-race prediction and instantiate the framework for happens-before and sync-preserving races, yielding efficient detection algorithms. Our happens-before algorithm runs in the same time as FastTrack but uses space that scales with $\log w$ as opposed to $\log |\sigma|$. For sync-preserving races, our algorithm runs faster and consumes significantly less space than SyncP. Our experiments validate the effectiveness of these short-race detection algorithms: they run more efficiently, use less memory, and detect significantly more races under the same budget, offering a reasonable balance between resource usage and predictive power.