An empirical comparison of monitoring algorithms for access anomaly detection
PPOPP '90 Proceedings of the second ACM SIGPLAN symposium on Principles & practice of parallel programming
On-the-fly detection of data races for programs with nested fork-join parallelism
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
On-the-fly detection of access anomalies in nested parallel loops
PADD '93 Proceedings of the 1993 ACM/ONR workshop on Parallel and distributed debugging
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
OpenMP: An Industry-Standard API for Shared-Memory Programming
IEEE Computational Science & Engineering
A practical tool for detecting races in OpenMP programs
PaCT'05 Proceedings of the 8th international conference on Parallel Computing Technologies
Efficient race verification for debugging programs with openMP directives
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
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Detecting races is important for debugging shared-memory parallel programs, because the races result in unintended nondeterministic executions of the program. On-the-fly technique to detect races uses a scalable labeling scheme which generates concurrency information of parallel threads without any globally-shared data structure. Two efficient schemes of scalable labeling, BD Labeling and NR Labeling, show the similar complexities in space and time, but their actual efficiencies have been compared empirically in no literature to the best of our knowledge. In this paper, we empirically compare these two labeling schemes by monitoring a set of OpenMP kernel programs with nested parallelism. The empirical results show that NR Labeling is more efficient than BD Labeling by at least 1.5 times in generating the thread labels, and by at least 3.5 times in using the labels to detect races in the kernel programs.