Abstract
Irregular array-type reductions represent a reoccurring algorithmic pattern in many scientific applications. Their scalable execution on modern systems is not trivial as their irregular memory access pattern prohibits an efficient use of the memory subsystem and costly techniques are needed to eliminate data races. Taking a closer look at algorithms, memory access patterns and support techniques reveals that a one-size-fits-all solution does not exist and approaches are needed that can adapt to individual properties while maintaining programming transparency. In this work we propose a solution framework that generalizes the concept of privatization to support a variety of techniques, implements an inspector-executor to provide memory access analytics to the runtime for automatic tuning and shows what language extensions are needed. A reference implementation in OmpSs, a task-parallel programming model, shows programmability and scalability of this solution.
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Acknowledgment
This work has been developed with the support of the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government and by the Spanish Ministry of Science and Innovation (contracts TIN2012-34557, and CAC2007-00052) by the Generalitat de Catalunya (contract 2009-SGR-980) and the Intel-BSC Exascale Lab collaboration project.
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Ciesko, J., Mateo, S., Teruel, X., Martorell, X., Ayguadé, E., Labarta, J. (2016). Supporting Adaptive Privatization Techniques for Irregular Array Reductions in Task-Parallel Programming Models. In: Maruyama, N., de Supinski, B., Wahib, M. (eds) OpenMP: Memory, Devices, and Tasks. IWOMP 2016. Lecture Notes in Computer Science(), vol 9903. Springer, Cham. https://doi.org/10.1007/978-3-319-45550-1_24
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DOI: https://doi.org/10.1007/978-3-319-45550-1_24
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