A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

Mahder Gebremedhin; Afshin Hemmati Moghadam; Peter Fritzson; Kristian Stavåker

Title:

Author:

Mahder Gebremedhin: Department of Computer and Information Science, Linköping University, Linköping, Sweden Afshin Hemmati Moghadam: Department of Computer and Information Science, Linköping University, Linköping, Sweden Peter Fritzson: Department of Computer and Information Science, Linköping University, Linköping, Sweden Kristian Stavåker: Department of Computer and Information Science, Linköping University, Linköping, Sweden

DOI:

10.3384/ecp12076393

Download:

Full text (pdf)

Year:

2012

Conference:

Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany

Issue:

076

Article no.:

041

Pages:

393-404

No. of pages:

Publication type:

Abstract and Fulltext

Published:

2012-11-19

ISBN:

978-91-7519-826-2

Series:

Linköping Electronic Conference Proceedings

ISSN (print):

1650-3686

ISSN (online):

1650-3740

Publisher:

Linköping University Electronic Press; Linköpings universitet

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New multi-core CPU and GPU architectures promise high computational power at a low cost if suitable computational algorithms can be developed. However; parallel programming for such architectures is usually non-portable; low-level and error-prone. To make the computational power of new multi-core architectures more easily available to Modelica modelers; we have developed the ParModelica algorithmic language extension to the high-level Modelica modeling language; together with a prototype implementation in the OpenModelica framework. This enables the Modelica modeler to express parallel algorithms directly at the Modelica language level. The generated code is portable between several multi-core architectures since it is based on the OpenCL programming model. The implementation has been evaluated on a benchmark suite containing models with matrix multiplication; Eigen value computation; and stationary heat conduction. Good speedups were obtained for large problem sizes on both multi-core CPUs and GPUs. To our knowledge; this is the first high-performing portable explicit parallel programming extension to Modelica.

Keywords: Parallel; Simulation; Benchmarking; Modelica; Compiler; GPU; OpenCL; Multi-Core

Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany

Author:

Mahder Gebremedhin, Afshin Hemmati Moghadam, Peter Fritzson, Kristian Stavåker

Title:

A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

DOI:

http://dx.doi.org/10.3384/ecp12076393

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Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany

Author:

Mahder Gebremedhin, Afshin Hemmati Moghadam, Peter Fritzson, Kristian Stavåker

Title:

A Data-Parallel Algorithmic Modelica Extension for Efficient Execution on Multi-Core Platforms

DOI:

https://doi.org10.3384/ecp12076393

Note: the following are taken directly from CrossRef

Citations:

Sumeyye Kaynak, Baran Kayna & Ahmet Ozmen (2020). GPU-Based Dynamic Solar Potential Estimation Tool Using 3D Plans. IEEE Access, 8: 45432. DOI: 10.1109/ACCESS.2020.2978590

Salah Eddine Saidi, Nicolas Pernet, Yves Sorel, A. Anciaux-Sedrakia & Q. H. Tran (2019). A method for parallel scheduling of multi-rate co-simulation on multi-core platforms. Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles, 74: 49. DOI: 10.2516/ogst/2019009

Peter Fritzson, Adrian Pop, Karim Abdelhak, Adeel Ashgar, Bernhard Bachmann, Willi Braun, Daniel Bouskela, Robert Braun, Lena Buffoni, Francesco Casella, Rodrigo Castro, Rüdiger Franke, Dag Fritzson, Mahder Gebremedhin, Andreas Heuermann, Bernt Lie, Alachew Mengist, Lars Mikelsons, Kannan Moudgalya, Lennart Ochel, Arunkumar Palanisamy, Vitalij Ruge, Wladimir Schamai, Martin Sjölund, Bernhard Thiele, John Tinnerhol & Per Östlund (2020). The OpenModelica Integrated Environment for Modeling, Simulation, and Model-Based Development. Modeling, Identification and Control: A Norwegian Research Bulletin, 41(4): 241. DOI: 10.4173/mic.2020.4.1

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