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Modelica PARallel benchmark suite (MPAR) – A test suite for Evaluating the Performance of Parallel Simulations of Modelica Models (Computer Project)

Using the object-oriented, equation-based modeling language Modelica, it is possible to model and simulate computationally intensive models. To reduce the simulation time, a desirable approach is to perform the simulations on parallel multi-core platforms.

For this purpose, several works have been carried out so far, the most recent one includes language enhancements with explicit parallel programing language constructs in the algorithmic parts of the Modelica language. This extension automatically generates parallel simulation code for execution on OpenCL-enabled platforms, and it has been implemented in the open-source OpenModelica environment.

However, to ensure that this extension as well as future developments regarding parallel simulations of Modelica models are feasible, performing a systematic benchmarking with respect to a set of appropriate Modelica models is essential, which is the main focus of study in this thesis.

In this thesis a benchmark test suite containing computationally intensive Modelica models which are relevant for parallel simulations is presented. The suite is used in this thesis as a means for evaluating the feasibility and performance measurements of the generated OpenCL code when using the new Modelica language extension.

In addition, several considerations and suggestions on how the modeler can efficiently parallelize sequential models to achieve better performance on OpenCL-enabled GPUs and multi-coreCPUs are also given.

The measurements have been done for both sequential and parallel implementations of the benchmark suite using the generated code from the OpenModelica compiler on different hardware configurations including single and multi-core CPUs as well as GPUs.

The gained results in this thesis show that simulating Modelica models using OpenCL as a target language is very feasible. In addition, it is concluded that for models with large data sizes and great level of parallelism, it is possible to achieve considerable speedup on GPUs compared to single and multi-core CPUs.
Source: Linköping University
Author: Hemmati Moghadam, Afshin

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