Dynamic Data Race Prediction
Authors
Umang Mathur
Date and Time
March 19th 2021, 14:15 - 15:00
Location
Zoom: https://uu-se.zoom.us/j/61183647419
Abstract
Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism introduces subtle errors that are both hard to detect and to reproduce. Data races are arguably the most insidious amongst concurrency bugs and extensive research efforts have been dedicated to effectively detect them.
A data race occurs when memory-conflicting actions are executed concurrently. Consequently, considerable effort has been made towards developing efficient techniques for race detection. The preferred approach to detect data races is through dynamic analysis, where one observes an execution of a concurrent programs and checks for the presence of data races in the execution observed.
Traditional dynamic race detectors rely on Lamport's happens-before (HB) partial order, which can be conservative and are often unable to discover simple data races, even after executing the program several times.
In this talk, I will talk about dynamic data race prediction, where one aims to expose data races missed by traditional dynamic race detectors (such as those based on HB) by inferring data races in alternate executions of the underlying program, without re-executing it.
In this talk, I will talk about the fundamentals of and some recent algorithmic advances in data race prediction.