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DASH: A C++ PGAS Library for Distributed Data Structures and Parallel Algorithms

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Title: DASH: A C++ PGAS Library for Distributed Data Structures and Parallel Algorithms
Authors: Fürlinger, Karl, Fuchs, Tobias, Kowalewski, Roger
Publication Year: 2016
Collection: Computer Science
Subject Terms: Computer Science - Distributed, Parallel, and Cluster Computing
More Details: We present DASH, a C++ template library that offers distributed data structures and parallel algorithms and implements a compiler-free PGAS (partitioned global address space) approach. DASH offers many productivity and performance features such as global-view data structures, efficient support for the owner-computes model, flexible multidimensional data distribution schemes and inter-operability with STL (standard template library) algorithms. DASH also features a flexible representation of the parallel target machine and allows the exploitation of several hierarchically organized levels of locality through a concept of Teams. We evaluate DASH on a number of benchmark applications and we port a scientific proxy application using the MPI two-sided model to DASH. We find that DASH offers excellent productivity and performance and demonstrate scalability up to 9800 cores.
Comment: Accepted for publication at HPCC 2016, 12-14 December 2016, Syndey Australia
Document Type: Working Paper
Access URL: http://arxiv.org/abs/1610.01482
Accession Number: edsarx.1610.01482
Database: arXiv
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  Data: DASH: A C++ PGAS Library for Distributed Data Structures and Parallel Algorithms
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  Data: We present DASH, a C++ template library that offers distributed data structures and parallel algorithms and implements a compiler-free PGAS (partitioned global address space) approach. DASH offers many productivity and performance features such as global-view data structures, efficient support for the owner-computes model, flexible multidimensional data distribution schemes and inter-operability with STL (standard template library) algorithms. DASH also features a flexible representation of the parallel target machine and allows the exploitation of several hierarchically organized levels of locality through a concept of Teams. We evaluate DASH on a number of benchmark applications and we port a scientific proxy application using the MPI two-sided model to DASH. We find that DASH offers excellent productivity and performance and demonstrate scalability up to 9800 cores.<br />Comment: Accepted for publication at HPCC 2016, 12-14 December 2016, Syndey Australia
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      – SubjectFull: Computer Science - Distributed, Parallel, and Cluster Computing
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