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 504645
Title Model-driven product line engineering for mapping parallel algorithms to parallel computing platforms
Author(s) Arkin, Ethem; Tekinerdogan, Bedir
Source In: MODELSWARD 2016 - Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development. - SciTePress - ISBN 9789897581687 - p. 347 - 354.
Event 4th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2016, Rome, 2016-02-19/2016-02-21
Department(s) Information Technology
Publication type Contribution in proceedings
Publication year 2016
Keyword(s) Model Driven Software Development - Parallel Computing - Product Line Engineering - Tool Support

Mapping parallel algorithms to parallel computing platforms requires several activities such as the analysis of the parallel algorithm, the definition of the logical configuration of the platform, the mapping of the algorithm to the logical configuration platform and the implementation of the source code. Applying this process from scratch for each parallel algorithm is usually time consuming and cumbersome. Moreover, for large platforms this overall process becomes intractable for the human engineer. To support systematic reuse we propose to adopt a model-driven product line engineering approach for mapping parallel algorithms to parallel computing platforms. Using model-driven transformation patterns we support the generation of logical configurations of the computing platform and the generation of the parallel source code that runs on the parallel computing platform nodes. The overall approach is illustrated for mapping an example parallel algorithm to parallel computing platforms.

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