Coarse-Grain Optimization and Code Generation for Embedded Multicore Systems

As processors and systems-on-chip increasingly become multicore, parallel programming remains a difficult, time-consuming and complicated task. End users who are not parallel programming experts have a need to exploit such processors and architectures, using state of the art fourth generation of hig...

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Bibliographic Details
Published in2013 Euromicro Conference on Digital System Design pp. 379 - 386
Main Authors Goulas, George, Valouxis, Christos, Alefragis, Panayiotis, Voros, Nikolaos S., Oey, Oliver, Stripf, Timo, Bruckschloegl, Thomas, Becker, Juergen, Gogos, Christos, El Moussawi, Ali, Naullet, Maxime, Yuki, Tomofumi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Algorithms
Coarse-Grain Optimization
Data mining
Multicore processing
Optimization
Parallel Code Generation
Parallel processing
Program processors
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Summary:As processors and systems-on-chip increasingly become multicore, parallel programming remains a difficult, time-consuming and complicated task. End users who are not parallel programming experts have a need to exploit such processors and architectures, using state of the art fourth generation of high programming languages, like Scilab or MATLAB. The ALMA toolset addresses this problem by receiving Scilab code as input and produces parallel code for embedded multiprocessor systems on chip, using platform quasi-agnostic optimisations. In this paper, coarse grain parallelism extraction and optimization issues as well as parallel code generation for the ALMA toolset are discussed.
DOI:10.1109/DSD.2013.48