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Fuchs, Tobias; Fürlinger, Karl (2016): A Multi-Dimensional Distributed Array Abstraction for PGAS. 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 12-14 Dec. 2016, Sydney, Australia.
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Abstract

DASH is a realization of the PGAS (partitioned global address space) model in the form of a C++ template library without the need for a custom PGAS (pre-) compiler. We present the DASH NArray concept, a multidimensional array abstraction designed as an underlying container for stenciland dense numerical applications. After introducing fundamental programming concepts used in DASH, we explain how these have been extended by multidimensional capabilities in the NArray abstraction. Focusing on matrix-matrix multiplication in a case study, we then discuss an implementation of the SUMMA algorithm for dense matrix multiplication to demonstrate how the DASH NArray facilitates portable efficiency and simplifies the design of efficient algorithms due to its explicit support for locality-based operations. Finally, we evaluate the performance of the SUMMA algorithm based on the NArray abstraction against established implementations of DGEMM and PDGEMM. In combination with mechanisms for automatic optimization of logical process topology and domain decomposition, our implementation yields highly competitive results without manual tuning, significantly outperforming Intel MKL and PLASMA in node-level use cases as well as ScaLAPACK in highly distributed scenarios.