Abstract
Static data-race detection is a powerful tool by providing clues for dynamic approaches to only instrument certain memory accesses. However, static data-race analysis suffers from high false positive rate. A key reason is that static analysis overestimates the set of shared objects a thread can access. We propose thread specialization to distinguish threads statically. By fixing the number of threads as well as the ID assigned to each thread, a program can be transformed to a simplified version. Static data-race analysis on this simplified program can infer the range of addresses accessed by each thread more accurately. Our approach prunes false positives by an average of 89.2% and reduces dynamic instrumentation by an average of 63.4% in seven benchmarks.
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Chen, C., Lu, K., Wang, X., Zhou, X., Fang, L. (2013). Pruning False Positives of Static Data-Race Detection via Thread Specialization. In: Wu, C., Cohen, A. (eds) Advanced Parallel Processing Technologies. APPT 2013. Lecture Notes in Computer Science, vol 8299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45293-2_6
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DOI: https://doi.org/10.1007/978-3-642-45293-2_6
Publisher Name: Springer, Berlin, Heidelberg
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