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number of GPU cores employed for data race detection, GUARD is able to perform
data race detection for 8-core, 16-core, and 32-core CPUs with near-zero performance
overhead. With minimal hardware support, GUARD can be invoked for data race de-
tection with negligible performance impact. Overall, GUARD proves to be a powerful
tool in the parallel programming environment, necessitated by the emergence of many-
core processors, and facilitated by the development of heterogeneous architectures with
on-chip data-parallel cores.
Acknowledgments.
This work is supported in part by National Science Foundation
grants CCF-0916583 and CPS-0931931. We would like to thank all anonymous review-
ers for their constructive comments that helped to improve the quality of this paper. We
would also like to thank Jieming Yin and Ragavendra Natarajan for their suggestions to
improve the paper.
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