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- W1570341479 abstract "Data races are errors caused by uncoordinated access in parallel programs, resulting in non-deterministic program execution. Therefore, the main focus of the current paper is to 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 examines the 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.KeywordsParallel ProgramShared VariableNest LoopDecomposition TreeData RaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W1570341479 created "2016-06-24" @default.
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- W1570341479 date "2002-01-01" @default.
- W1570341479 modified "2023-09-23" @default.
- W1570341479 title "Space-Efficient First Race Detection in Shared Memory Programs with Nested Parallelism" @default.
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- W1570341479 doi "https://doi.org/10.1007/3-540-48051-x_26" @default.
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