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.
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Ha, KS., Ryu, EK., Yoo, KY. (2002). Space-Efficient First Race Detection in Shared Memory Programs with Nested Parallelism. In: Fagerholm, J., Haataja, J., Järvinen, J., Lyly, M., Råback, P., Savolainen, V. (eds) Applied Parallel Computing. PARA 2002. Lecture Notes in Computer Science, vol 2367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48051-X_26
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DOI: https://doi.org/10.1007/3-540-48051-X_26
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