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

References:

[1] Modelica Association. The Modelica Language Specification Version 3.2; March 24th 2010. http://www.modelica.org. Modelica Association. Modelica Standard Library 3.1. Aug. 2009. http://www.modelica.org./

[2] Open Source Modelica Consortium. OpenModelica System Documentation Version 1.8.1; April 2012. http://www.openmodelica.org/

[3] Peter Fritzson. Principles of Object-Oriented Modeling and Simulation with Modelica 2.1. Wiley-IEEE Press; 2004. doi: 10.1109/9780470545669.

[4] Peter Aronsson. Automatic Parallelization of Equation-Based Simulation Programs; PhD thesis; Dissertation No. 1022; Linköping University; 2006.

[5] Håkan Lundvall. Automatic Parallelization using Pipelining for Equation-Based Simulation Languages; Licentiate thesis No. 1381; Linköping University; 2008.

[6] Håkan Lundvall; Kristian Stavåker; Peter Fritzson; Christoph Kessler: Automatic Parallelization of Simulation Code for Equation-based Models with Software Pipelining and Measure-ments on Three Platforms. MCC’08 Workshop; Ronneby; Sweden; November 27-28; 2008.

[7] Per Östlund. Simulation of Modelica Models on the CUDA Architecture. Master Thesis. LIU-IDA/LITH-EX-A-09/062-SE. Linköping Univer-sity; 2009.

[8] Kristian Stavåker; Peter Fritzson. Generation of Simulation Code from Equation-Based Models for Execution on CUDA-Enabled GPUs. MCC’10 Workshop; Gothenburg; Sweden; November 18-19; 2010.

[9] Matthias Korch and Thomas Rauber. Scalable parallel rk solvers for odes derived by the method of lines. In Harald Kosch; Laszlo Böszörményi; and Hermann Hellwagner; editors; Euro-Par; vol-ume 2790 of Lecture Notes in Computer Science; pages 830-839. Springer; 2003.

[10] Christoph Kessler and Peter Fritzson. NestStep-Modelica – Mathematical Modeling and Bulk-Synchronous Parallel Simulation. In Proc. of PARA’06; Umeå; June 19-20; 2006. In Lecture Notes of Computer Science (LNCS) Vol 4699; pp 1006-1015; Springer Verlag; 2006.

[11] Martin Sjölund; Robert Braun; Peter Fritzson and Petter Krus. Towards Efficient Distributed Simu-lation in Modelica using Transmission Line Mod-eling. In Proceedings of the 3rd International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools; (EOOLT’2010); Published by Linköping University Electronic Press; www.ep.liu.se; In conjunction with MOD-ELS’2010; Oslo; Norway; Oct 3; 2010.

[12] Francois Cellier and Ernesto Kofman. Continuous System Simulation. Springer; 2006.

[13] Khronos Group; Open Standards for Media Authoring and Acceleration; OpenCL 1.1; accessed Sept 15; 2011. http://www.khronos.org/opencl/

[14] The OpenCL Specication; Version: 1.1; Document Revision: 44; accessed June 30 2011. http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

[15] NVIDIA CUDA; accessed September 15 2011. http://www.nvidia.com/object/cuda home new.html

[16] NVIDIA CUDA programming guide; accessed 30 June 2011. http://developer.download.nvidia.com/ compute/cuda/4 0 rc2/toolkit/docs/CUDA C Pro-gramming Guide.pdf

[17] OpenCL Programming Guide for the CUDA Architecture; Appendix A; accessed June 30 2011. http://developer.download.nvidia.com/compute/DevZone/docs/html/OpenCL/doc/OpenCL Pro-gramming Guide.pdf

[18] AMD OpenCL; System Requirements & Driver Compatibility; accessed June 30 2011. http://developer.amd.com/sdks/AMDAPPSDK/pages/DriverCompatibility.aspx

[19] INTEL OpenCL; Technical Requirements; accessed June 30 2011. http://software.intel.com/enus/articles/opencl-release-notes/

[20] OpenCL Work-Item Built-In Functions; accessed June 30 2011. http://www.khronos.org/registry/cl/sdk/1.0/docs/man/xhtml/workItemFunctions.html

[21] Jack J. Dongarra; J. Bunch; Cleve Moler; and G. W. Stewart. LINPACK User’s Guide. SIAM; Philadelphia; PA; 1979.

[22] Ian N. Sneddon. Fourier Transforms. Dover Publications; 2010. ISBN-13: 978-0486685229.

[23] John H. Lienhard IV and John H. Lienhard V. A Heat Transfer Textbook. Phlogiston Press Cambridge; Massachusetts; U.S.A; 4th edition; 2011.

[24] Intel Xeon E5520 CPU Specifications; accessed October 28 2011. http://ark.intel.com/products/40200/Intel-Xeon-Processor-E5520-(8M-Cache-2 26-GHz-5 86-GTs-Intel-QPI)

[25] NVIDIA Tesla M2050 GPU Specifications; accessed June 30 2011. http://www.nvidia.com/docs/IO/43395/BD-05238-001 v03.pdf

[26] Cyril Faure. Real-time simulation of physical models toward hardware-in-the-loop validation. PhD Thesis. University of Paris East; October 2011.

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

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