Towards an Accurate GPU Data Race Detector
Ajay Nayak, Anubhab Ghosh, Arkaprava Basu

TL;DR
This paper introduces HGRD, a static analysis tool that leverages host code information to accurately detect GPU data races, outperforming existing dynamic and static methods in precision and recall.
Contribution
HGRD is a novel static analysis approach that combines host and GPU code analysis to detect data races with high accuracy and minimal false alarms.
Findings
HGRD detects all true data races missed by prior methods.
HGRD raises no false alarms unlike other static techniques.
HGRD outperforms dynamic techniques like iGUARD in race detection.
Abstract
Data races in GPU programs pose a threat to the reliability of GPU-accelerated software stacks. Prior works proposed various dynamic (runtime) and static (compile-time) techniques to detect races in GPU programs. However, dynamic techniques often miss critical races, as they require the races to manifest during testing. While static ones can catch such races, they often generate numerous false alarms by conservatively assuming values of variables/parameters that cannot ever occur during any execution of the program. We make a key observation that the host (CPU) code that launches GPU kernels contains crucial semantic information about the values that the GPU kernel's parameters can take during execution. Harnessing this hitherto overlooked information helps accurately detect data races in GPU kernel code. We create HGRD, a new state-of-the-art static analysis technique that performs a…
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