Reference : Unified Design for Parallel Execution of Coupled Simulations using the Discrete Parti...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/1427
Unified Design for Parallel Execution of Coupled Simulations using the Discrete Particle Method
English
Besseron, Xavier mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Hoffmann, Florian mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Michael, Mark mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Peters, Bernhard mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
2013
Proceedings of the Third International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering
Civil-Comp Press
Yes
International
Stirlingshire
United Kingdom
Third International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering
March 2013
Pécs
Hungary
[en] Granular matter ; Discrete element method ; Domain decomposition ; Parallel computing ; Load-balancing
[en] This paper presents the enhanced design of the Discrete Particle Method (DPM), a
simulation tool which provides high quality and fast simulations to solve a broad range
industrial processes involving granular materials. It enables to resolve mechanical and
thermodynamics problems through different simulation modules (motions, chemical
conversion). This new design allows to transparently couple the simulation modules
in parallel execution. It relies on a unified interface and timebase of the simulation
modules and a flexible decomposition in cells of the simulation space. Experimental
results study the behavior of the Orthogonal Recursive Bisection (ORB) partitioning
algorithm. A good scalability is achieved as the parallel execution on a distributed
platform provides a 17-times speedup using 64 processes.
University of Luxembourg: High Performance Computing - ULHPC ; University of Luxembourg: Luxembourg XDEM Research Centre - LuXDEM
http://hdl.handle.net/10993/1427
10.4203/ccp.101.49

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