HMTRace: Hardware-Assisted Memory-Tagging based Dynamic Data Race Detection
Jaidev Shastri, Xiaoguang Wang, Basavesh Ammanaghatta Shivakumar,, Freek Verbeek, Binoy Ravindran

TL;DR
HMTRace is a low-overhead, hardware-assisted dynamic data race detection framework for multi-threaded C applications on Armv8.5-A, achieving high accuracy with minimal performance impact.
Contribution
It introduces a novel memory tagging extension-based approach for efficient and precise data race detection in user-space applications.
Findings
F1-score of 0.86 in race detection
4.01% average execution time overhead
54.31% peak memory overhead
Abstract
Data race, a category of insidious software concurrency bugs, is often challenging and resource-intensive to detect and debug. Existing dynamic race detection tools incur significant execution time and memory overhead while exhibiting high false positives. This paper proposes HMTRace, a novel Armv8.5-A memory tag extension (MTE) based dynamic data race detection framework, emphasizing low compute and memory requirements while maintaining high accuracy and precision. HMTRace supports race detection in userspace OpenMP- and Pthread-based multi-threaded C applications. HMTRace showcases a combined f1-score of 0.86 while incurring a mean execution time overhead of 4.01% and peak memory (RSS) overhead of 54.31%. HMTRace also does not report false positives, asserting all reported races.
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Taxonomy
TopicsMachine Learning and Data Classification · Software Engineering Research · Advanced Malware Detection Techniques
