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

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

doi:10.3384/ecp12076393

Cited by 16 publications

(8 citation statements)

References 5 publications

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“…Its long-term development is supported by a non-profit organization The Open Source Modelica Consortium (OSMC) 9 . The Programming Environments Laboratory (PELAB) at Linköping University, together with OSMC, is developing the OpenModelica Compiler (OMC) for the Modelica language (including the MetaModelica [89], ParModelica [48] and Optimica [4] language extensions) and the accompanying simulation environment.…”

Section: Openmodelicamentioning

confidence: 99%

Automatic and Explicit Parallelization Approaches for Equation Based Mathematical Modeling and Simulation

Gebremedhin

2019

Linköping Studies in Science and Technology. Dissertations

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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.

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Section: Openmodelicamentioning

confidence: 99%

Automatic and Explicit Parallelization Approaches for Equation Based Mathematical Modeling and Simulation

Gebremedhin

2019

Linköping Studies in Science and Technology. Dissertations

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“…In order to achieve (co-)simulation acceleration using multi-core execution, different approaches are possible and were already explored. From a user point of view, it is possible to modify the model design in order to prepare its multi-core execution, for example by using models that are developed using parallel computing libraries as in (Gebremedhin et al 2012). Using parallel solvers as in (Elmqvist et al 2014) is another alternative.…”

Section: Fmu a Fmu Bmentioning

confidence: 99%

Automatic Parallelization of Multi-Rate FMI-based Co-Simulation on Multi-Core

Saidi

Pernet

Sorel

2017

TMS/DEVS Symposium on Theory of Modeling &Amp; Simulation (TMS/DEVS 2017)

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Co-simulation refers to simulating a complex system using several coupled numerical models. Engineers define the rate of data exchange between the models by setting communication steps. FMI is a standardized interface which easily allows coupling and co-simulation of numerical models. The RCOSIM approach allows the parallelization on multi-core processors of co-simulations using the FMI standard. In this paper, we tackle the limitations of this approach. First, we extend the co-simulation to multi-rate, i.e. with different communication steps. We present graph transformation rules and an algorithm that allow the execution of each model at its respective rate while ensuring correct data exchange between models. Second, we present an acyclic orientation heuristic for handling mutual exclusion constraints between operations that belong to the same model due to the non-thread-safe implementation of FMI. We evaluate the obtained speedup on a multi-core processor and the effect on the accuracy of the numerical results.

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“…In order to achieve simulation acceleration using multicore execution, different approaches are possible and were already explored. From a user point of view, it is 1 http://www.xmodsoftware.com/ possible to modify the model design in order to prepare its multi-core execution, for example by using marked functions or Modelica extensions as in (Elmqvist et al, 2015;Gebremedhin et al, 2012). From a modeling tool provider point of view, if providing OpenMP ready libraries is possible, the key feature for simulation acceleration is to provide techniques which offer speedup whatever the model is.…”

Section: Related Workmentioning

confidence: 99%

The First Japanese Modelica Conferences, May 23-24, Tokyo, Japan

Hirano

Andreasson²

2016

Linköping Electronic Conference Proceedings

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What should Model Based Development aim for? Vehicle systems have become vast and complex to attain a high level of functionality. It is expected to evolve at an even more accelerated rate. Model Based Development (MBD) has become essential to accomplish such a high level of manufacturing process in a short term. I will show what has been targeted and realized in MBD to date, by giving some examples of past developments, and will look into an ideal manufacturing process.

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

Cited by 16 publications

References 5 publications

Automatic and Explicit Parallelization Approaches for Equation Based Mathematical Modeling and Simulation

Automatic and Explicit Parallelization Approaches for Equation Based Mathematical Modeling and Simulation

Automatic Parallelization of Multi-Rate FMI-based Co-Simulation on Multi-Core

The First Japanese Modelica Conferences, May 23-24, Tokyo, Japan

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