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  • 1.
    Bachmann, Bernhard
    et al.
    Dept. Mathematics and Engineering, University of Applied Sciences, Bielefeld, Germany.
    Ochel, Lennart
    Dept. Mathematics and Engineering, University of Applied Sciences, Bielefeld, Germany.
    Ruge, Vitalij
    Dept. Mathematics and Engineering, University of Applied Sciences, Bielefeld, Germany.
    Gebremedhin, Mahder
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory.
    Fritzson, Peter
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory.
    Nezhadali, Vaheed
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Eriksson, Lars
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Sivertsson, Martin
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Parallel Multiple-Shooting and Collocation Optimization with OpenModelica2012In: Proceedings of the 9th International MODELICA Conference; September 3-5; 2012; Munich; Germany, Linköping University Electronic Press, 2012, p. 659-668, article id 067Conference paper (Refereed)
    Abstract [en]

    Nonlinear model predictive control (NMPC) has become increasingly important for today’s control engineers during the last decade. In order to apply NMPC a nonlinear optimal control problem (NOCP) must be solved which needs a high computational effort.

    State-of-the-art solution algorithms are based on multiple shooting or collocation algorithms; which are required to solve the underlying dynamic model formulation. This paper describes a general discretization scheme applied to the dynamic model description which can be further concretized to reproduce the mul-tiple shooting or collocation approach. Furthermore; this approach can be refined to represent a total collocation method in order to solve the underlying NOCP much more efficiently. Further speedup of optimization has been achieved by parallelizing the calculation of model specific parts (e.g. constraints; Jacobians; etc.) and is presented in the coming sections.

    The corresponding discretized optimization problem has been solved by the interior optimizer Ipopt. The proposed parallelized algorithms have been tested on different applications. As industrial relevant application an optimal control of a Diesel-Electric power train has been investigated. The modeling and problem description has been done in Optimica and Modelica. The simulation has been performed using OpenModelica. Speedup curves for parallel execution are presented.

  • 2.
    Gebremedhin, Mahder
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Automatic and Explicit Parallelization Approaches for Equation Based Mathematical Modeling and Simulation2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The move from single-core processor systems to multi-core and manyprocessor systems comes with the requirement of implementing computations in a way that can utilize these multiple computational units efficiently. This task of writing efficient parallel algorithms will not be possible without improving programming languages and compilers to provide the supporting mechanisms. Computer aided mathematical modelling and simulation is one of the most computationally intensive areas of computer science. Even simplified models of physical systems can impose a considerable computational load on the processors at hand. Being able to take advantage of the potential computational power provided by multi-core systems is vital in this area of application. This thesis tries to address how to take advantage of the potential computational power provided by these modern processors in order to improve the performance of simulations, especially for models in the Modelica modelling language compiled and simulated using the OpenModelica compiler and run-time environment.

    Two approaches of utilizing the computational power provided by modern multi-core architectures for simulation of Mathematical models are presented in this thesis: Automatic and Explicit parallelization respectively. The Automatic approach presents the process of extracting and utilizing potential parallelism from equation systems in an automatic way without any need for extra effort from the modellers/programmers. This thesis explains new and improved methods together with improvements made to the OpenModelica compiler and a new accompanying task systems library for efficient representation, clustering, scheduling, profiling, and executing complex equation/ task systems with heavy dependencies. The Explicit parallelization approach allows utilizing parallelism with the help of the modeller or programmer. New programming constructs have been introduced to the Modelica language in order to enable modellers to express parallelized algorithms to take advantage of the computational capabilities provided by modern multicore CPUs and GPUs. The OpenModelica compiler has been improved accordingly to recognize and utilize the information from these new algorithmic constructs and to generate parallel code for enhanced computational performance, portable to a range of parallel architectures through the OpenCL standard.

  • 3.
    Gebremedhin, Mahder
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.
    Automatic and Explicit Parallelization Approaches for Mathematical Simulation Models2015Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The move from single core and processor systems to multi-core and many-processors systemscomes with the requirement of implementing computations in a way that can utilizethese multiple units eciently. This task of writing ecient multi-threaded algorithmswill not be possible with out improving programming languages and compilers to providethe mechanisms to do so. Computer aided mathematical modeling and simulationis one of the most computationally intensive areas of computer science. Even simpli-ed models of physical systems can impose a considerable amount of computational loadon the processors at hand. Being able to take advantage of the potential computationpower provided by multi-core systems is vital in this area of application. This thesis triesto address how we can take advantage of the potential computation power provided bythese modern processors to improve the performance of simulations. The work presentsimprovements for the Modelica modeling language and the OpenModelica compiler.

    Two approaches of utilizing the computational power provided by modern multi-corearchitectures are presented in this thesis: Automatic and Explicit parallelization. Therst approach presents the process of extracting and utilizing potential parallelism fromequation systems in an automatic way with out any need for extra eort from the modelers/programmers side. The thesis explains improvements made to the OpenModelicacompiler and presents the accompanying task systems library for ecient representation,clustering, scheduling proling and executing complex equation/task systems with heavydependencies. The Explicit parallelization approach explains the process of utilizing parallelismwith the help of the modeler or programmer. New programming constructs havebeen introduced to the Modelica language in order to enable modelers write parallelizedcode. the OpenModelica compiler has been improved accordingly to recognize and utilizethe information from this new algorithmic constructs and generate parallel code toimprove the performance of computations.

  • 4.
    Gebremedhin, Mahder
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology.
    Fritzson, Peter
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology.
    Automatic Task Based Analysis and Parallelization in the Context of Equation Based Languages2014In: EOOLT '14 Proceedings of the 6th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools, New York: ACM , 2014, p. 49-52Conference paper (Refereed)
    Abstract [en]

    This paper presents an automatic parallelization approach for handling complex task systems with heavy dependencies, including methods of analyzing dependencies, representing them in a convenient way, and processing the resulting task graph representation. We present a library-based task system representation, clustering, profiling, and scheduling approach to simplify the otherwise tedious process of parallelizing complex task systems. We have implemented a flexible and robust task system handling library to manipulate and parallelize these complex task systems on shared memory multi-core and multi-processor systems. The implementation has been developed as part of the OpenModelica simulation environment. We demonstrate methods of extracting and utilizing parallelism in the context of mathematical modeling languages.

  • 5.
    Moghadam, Afshin Hemmati
    et al.
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Gebremedhin, Mahder
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Stavåker, Kristian
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Fritzson, Peter
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Simulation and benchmarking of Modelica models on multi-core architectures with explicit parallel algorithmic language extensions2011In: Fourth Swedish Workshop on Multi-Core Computing MCC-2011 / [ed] Kessler, Christoph, 2011, Vol. S. 109-114, p. 109-114Conference paper (Other academic)
    Abstract [en]

    In this paper we introduce new parallel programming language construcructs which can be used in the algorithmic parts of Modelica models, and we present a benchmark test suite of suitable algorithmic Modelica models that makes use of the new constructs (such as models containing large matrix computations). We provide measurements of simulating three models fraom this benchmark test suite using single-core and multi-core CPUs as well as GPUs. 

  • 6.
    Shitahun, Alachew
    et al.
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Ruge, Vitalij
    University of Applied Sciences, Bielefeld, Germany.
    Gebremedhin, Mahder
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Bachmann, Bernhard
    University of Applied Sciences, Bielefeld, Germany.
    Eriksson, Lars
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
    Andersson, Joel
    Diehl, Moritz
    Engineering Center (OPTEC), Leuven, Belgium.
    Fritzson, Peter
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Model-Based Dynamic Optimization with OpenModelica and CasADi2013In: IFAC-AAC 2013, 2013, p. 446-451Conference paper (Refereed)
    Abstract [en]

    This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical optimization. The coupling uses a standardized XML format for exchange of differential-algebraic equations (DAE) models. OpenModelica supports export of models written in Modelica and the optimization language extension using this XML format, while CasADi supports import of models represented in this format. This allows users to define optimal control problems (OCP) using Modelica and optimization language specification, and solve the underlying model formulation using a range of optimization methods, including direct collocation and direct multiple shooting. The proposed solution has been tested on several industrially relevant optimal control problems, including a diesel-electric power train.

  • 7.
    Sjölund, Martin
    et al.
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Gebremedhin, Mahder
    Linköping University, Department of Computer and Information Science.
    Fritzson, Peter
    Linköping University, Department of Computer and Information Science, PELAB - Programming Environment Laboratory. Linköping University, The Institute of Technology.
    Parallelizing Equation-Based Models for Simulation on Multi-Core Platforms by Utilizing Model Structure2013Conference paper (Refereed)
    Abstract [en]

    In today’s world of high tech manufacturing and computer-aided design simulations of models is at the heart of the whole manufacturing process. Trying to represent and study the variables of real world models using simulation computer programs can turn out to be a very expensive and time consuming task. On the other hand advancements in modern multi-core CPUs promise remarkable computational power. Modern modeling environments provide different optimization and parallelization options to take advantage of the available computational power. Some of these parallelization approaches are based on automatically extracting parallelism with the help of the model compiler or translator. Another approach is to provide the model programmers with the necessary language constructs to express any potential parallelism in their models.

    In this paper we present an automatic parallelization approach for Modelica models using Transmission Line Modeling (TLM). TLM is suitable for parallel simulations because larger models can be partitioned into smaller independent sub-models. TLM introduces parallelism into the system by decoupling subsystems using delays greater than the step size of the numerical solver. A prototype has been implemented in the OpenModelica Compiler (OMC) framework. Our approach re-uses the dependency analysis from the sequential translation step of OMC. With the help of the dependency analysis information the set of equations for a model is partitioned into a number of sub-systems. The resulting independent sub-systems are scheduled and executed in parallel. The run-time system for OMC has been improved to provide thread safety and handle parallelism while keeping the introduced overhead to minimum for normal sequential operation and maintaining portability.

1 - 7 of 7
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