Model-driven transformations for mapping parallel algorithms on parallel computing platforms

Date
2013
Authors
Arkin, E.
Tekinerdoğan, Bedir
Advisor
Instructor
Source Title
2nd International Workshop on Model-Driven Engineering for High Performance and Cloud computing (MDHPCL 2013)
Print ISSN
Electronic ISSN
Publisher
MDHPCL
Volume
1118
Issue
Pages
63 - 72
Language
English
Type
Conference Paper
Journal Title
Journal ISSN
Volume Title
Abstract

One of the important problems in parallel computing is the mapping of the parallel algorithm to the parallel computing platform. Hereby, for each parallel node the corresponding code for the parallel nodes must be implemented. For platforms with a limited number of processing nodes this can be done manually. However, in case the parallel computing platform consists of hundreds of thousands of processing nodes then the manual coding of the parallel algorithms becomes intractable and error-prone. Moreover, a change of the parallel computing platform requires considerable effort and time of coding. In this paper we present a model-driven approach for generating the code of selected parallel algorithms to be mapped on parallel computing platforms. We describe the required platform independent metamodel, and the model-to-model and the model-to-text transformation patterns. We illustrate our approach for the parallel matrix multiplication algorithm. Copyright © 2013 for the individual papers by the papers' authors.

Course
Other identifiers
Book Title
Keywords
Domain specific language, High performance computing, Model driven software development, Parallel computing, Tool support, Algorithms, Cloud computing, Codes (symbols), Computational linguistics, Computer programming languages, Mapping, Parallel algorithms, Parallel architectures, Parallel processing systems, Problem oriented languages, Software design, Domain specific languages, High performance computing, Model driven approach, Model to text transformations, Model-Driven Software Development, Parallel computing platform, Parallel matrix multiplication algorithms, Tool support, Distributed computer systems
Citation
Published Version (Please cite this version)