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
Improving the accuracy of data race detection
PPOPP '91 Proceedings of the third ACM SIGPLAN symposium on Principles and practice of parallel programming
Race Frontier: reproducing data races in parallel-program debugging
PPOPP '91 Proceedings of the third ACM SIGPLAN symposium on Principles and 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
What are race conditions?: Some issues and formalizations
ACM Letters on Programming Languages and Systems (LOPLAS)
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
Scalable on-the-fly detection of the first races in parallel programs
ICS '98 Proceedings of the 12th international conference on Supercomputing
Scalable Monitoring Technique for Detecting Races in Parallel Programs
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Space efficient data race detection for parallel programs with series-parallel task graphs
PDP '95 Proceedings of the 3rd Euromicro Workshop on Parallel and Distributed Processing
Detecting the First Races in Parallel Programs with Ordered Synchronization
ICPADS '98 Proceedings of the 1998 International Conference on Parallel and Distributed Systems
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Data races are errors caused by uncoordinated accessin parallel programs, resulting in non-deterministic program execution. Therefore, the main focus of the current paper isto create efficient on-the-fly detection of data races based on minimizing the shared data structures, thereby reducing the space overhead required to maintain the access history and concurrency information during an execution. Accordingly, a space efficient method is proposed for detecting first races, since their detection can eliminate other races. To reduce the storage requirements, the proposed method uses a sequential monitoring technique and decomposition tree, checks the logical concurrency among the threads, and examinesthe root events. The programs considered in this paper have a series-parallel graph with a fork-join. The resulting space complexity is O(VN), where V is the number of shared variables, T is the maximum parallelism, and N is the nesting depth of the program.