Towards Providing Low-Overhead Data Race Detection for Large OpenMP Applications
Joachim Protze, Simone Atzeni, Dong H. Ahn, Martin Schulz, Ganesh Lalitha Gopalakrishnan, Matthias Müller, Ignacio Laguna, Zvonimir Rakamarić, Greg L. Lee · 2014
Neither static nor dynamic data race detection methods, by themselves, have proven to be sufficient for large HPC applications, as they often result in high runtime overheads and/or low race-checking accuracy. While combined static and dynamic approaches can fare better, creating such combinations, in practice, requires attention to many details. Specifically, existing state-of-the-art dynamic race detectors are aimed at low-level threading models, and cannot handle high-level models such as OpenMP. Further, they do not provide mechanisms by which static analysis methods can target selected regions of code with sufficient precision. In this paper, we present our solutions to both challenges. Specifically, we identify patterns within OpenMP runtimes that tend to mislead existing dynamic race checkers and provide mechanisms that help establish an explicit happens-before relation to prevent such misleading checks. We also implement a fine-grained blacklist mechanism to allow a runtime analyzer to exclude regions of code at line number granularity. We support race checking by adapting ThreadSanitizer, a mature data-race checker developed at Google that is now an integral part of Clang and GCC; and we have implemented our techniques within the state-of-the-art Intel OpenMP Runtime. Our results demonstrate that these techniques can significantly improve runtime analysis accuracy and overhead in the context of data race checking of OpenMP applications.