Title: Dynamic Data Race Prediction: Fundamentals and Advances

Speaker: Umang Mathur    (bio) (bio)

Umang Mathur is an Assistant Professor at the National University of Singapore. He received his PhD from the University of Illinois at Urbana Champaign in 2021 and was an NTT Research Fellow at the Simons Institute for the Theory of Computing at Berkeley. His research interests lie in the use of formal methods and logic for answering design, analysis and implementation questions in programming languages, software engineering and systems. He is a recipient of a Google Research Award (2022), Google PhD Fellowship (2019), an ACM SIGSOFT Distinguished Paper Award at ESEC/FSE'18, a Best Paper Award at ASPLOS'22 and was invited as a Young Researcher at the 8th Heidelberg Laureate Forum.

When: Tuesday, 03 January 2023 at 1900 hrs (IST)       

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 program 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.

Dynamic data race prediction aims to expose data races, that can be otherwise 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 recent algorithmic advances in data race prediction.