Abstract
DASH is a realization of the PGAS (partitioned global address space) programming model in the form of a C++ template library. It provides a multi-dimensional array abstraction which is typically used as an underlying container for stencil- and dense matrix operations. Efficiency of operations on a distributed multi-dimensional array highly depends on the distribution of its elements to processes and the communication strategy used to propagate values between them. Locality can only be improved by employing an optimal distribution that is specific to the implementation of the algorithm, run-time parameters such as node topology, and numerous additional aspects. Application developers do not know these implications which also might change in future releases of DASH. In the following, we identify fundamental properties of distribution patterns that are prevalent in existing HPC applications. We describe a classification scheme of multi-dimensional distributions based on these properties and demonstrate how distribution patterns can be optimized for locality and communication avoidance automatically and, to a great extent, at compile-time.
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We gratefully acknowledge funding by the German Research Foundation (DFG) through the German Priority Program 1648 Software for Exascale Computing (SPPEXA).
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Fuchs, T., Fürlinger, K. (2016). Expressing and Exploiting Multi-Dimensional Locality in DASH. In: Bungartz, HJ., Neumann, P., Nagel, W. (eds) Software for Exascale Computing - SPPEXA 2013-2015. Lecture Notes in Computational Science and Engineering, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-40528-5_15
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DOI: https://doi.org/10.1007/978-3-319-40528-5_15
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