Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 499476
Title Systematic approach for deriving feasible mappings of parallel algorithms to parallel computing platforms
Author(s) Arkin, Ethem; Tekinerdogan, Bedir; Imre, Kayhan M.
Source Concurrency Computation 29 (2017)1. - ISSN 1532-0626 - 26 p.
Department(s) Information Technology
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) Architecture viewpoints - Model-driven software development - Parallel computing - Tool support

The need for high-performance computing together with the increasing trend from single processor to parallel computer architectures has leveraged the adoption of parallel computing. To benefit from parallel computing power, usually parallel algorithms are defined that can be mapped and executed on parallel computing platforms. In general, different alternative mappings can be defined each with different performance. For small computing platforms with a limited number of processing nodes, the mapping process can be carried out manually. However, for large-scale parallel computing platforms in which hundreds of thousands of processing nodes are applied, the number of possible mapping alternatives increases dramatically, and the mapping process becomes intractable for the human engineer. To assist the parallel computing engineer, we provide a systematic approach to derive feasible mapping alternatives of parallel algorithms to parallel computing platforms. The approach includes activities for modeling the parallel algorithm and parallel computing platform, generation of feasible mapping alternatives, generation of the deployment code, and finally the deployment of the generated code to the nodes. We evaluate our approach for deriving feasible mapping alternatives for four well-known parallel algorithms. The evaluation is based on both simulations and real executions of the generated mapping alternatives.

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