Evaluation and comparison of ten data race detection techniques Online publication date: Fri, 18-Aug-2017
by Zhen Yu; Zhen Yang; Xiaohong Su; Peijun Ma
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 10, No. 4/5, 2017
Abstract: Many techniques for dynamically detecting data races in multithreaded programs have been proposed. However, it is unclear how these techniques compare in terms of precision, overhead and scalability. This paper presents an experiment to evaluate ten data-race detection techniques on 100 small-scale or middle-scale C/C++ programs. The selected ten techniques, implemented in the same Maple framework, cover not only the classical but also the state-of-the-art in dynamical data-race detection. We compare the ten techniques and try to give reasonable explanations for why some techniques are weaker or stronger than other ones. Evaluation results show that no one technique performs perfectly for all programs according to the three criteria. Based on the evaluation and comparison, we give suggestions of which technique is the most suitable one to use when the target program exhibits particular characteristics. Later researchers can also benefit from our results to construct a better detection technique.
Online publication date: Fri, 18-Aug-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com