Abstract
In the early phases of an automotive industry development, design time can be substantially improved by using automated tools that assist engineers to perform repetitive and time consuming tasks, speeding up automotive products development and providing quality guarantees over otherwise error-prone processes. MERGE is such a tool from industry that prepares CAD models for electrophoretic deposition simulation. In this paper we describe the parallelization and optimization of MERGE for distributed memory parallel architectures. For this purpose we create a dynamic tree of tasks at runtime, analyze its load behavior and execute it through a master-worker compute paradigm based on different scheduling policies. Our implementation is based on a hybrid MPI-OpenMP version which results in a considerable improvement of both resource utilization and performance. Empirical performance results are presented for our new approach which achieve a speedup of up to 18 on an SMP cluster architecture.
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Acknowledgments
This research has been funded by the Austrian Research Promotion Agency under contract 834307 (AutoCore), and supported by the Austrian Ministry of Science BMWF as part of the UniInfrastrukturprogramm of the Focal Point Scientific Computing at the University of Innsbruck.
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Ayuso, L., Durillo, J.J., Kornberger, B., Schifko, M., Fahringer, T. (2016). Parallelization and Optimization of a CAD Model Processing Tool from the Automotive Industry to Distributed Memory Parallel Computers. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2015. Lecture Notes in Computer Science(), vol 9573. Springer, Cham. https://doi.org/10.1007/978-3-319-32149-3_36
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DOI: https://doi.org/10.1007/978-3-319-32149-3_36
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