Reference : Parallelizing XDEM: Load-balancing policies and efficiency, a study
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/32810
Parallelizing XDEM: Load-balancing policies and efficiency, a study
English
Rousset, Alban mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Besseron, Xavier 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 >]
Sep-2017
Yes
International
International Conference on Particle-Based Methods (PARTICLES17)
from 26-09-2017 to 28-09-2017
Hannover
Germany
[en] HPC ; load-balancing ; DEM
[en] In XDEM, the simulation domain is geometrically decomposed in regular fixed-size cells that are
used to distribute the workload between the processes. The role of the partitioning algorithm is to
distribute the cells among all the processes in order to balance the workload. To accomplish this task,
the partitioning algorithm relies on a computing/communication cost that has been estimated for each
cell. A proper estimation of these costs is fundamental to obtain pertinent results during this phase.
The study in the work is twofold. First, we integrate five partitioning algorithms (ORB, RCB, RIB, kway
and PhG) in the XDEM framework [1]. Most of these algorithms are implemented within the
Zoltan library [2], a parallel framework for partitioning and ordering problems. Secondly, we propose
different policies to estimate the computing cost and communication cost of the different cells
composing the simulation domain. Then, we present an experimental evaluation and a performance
comparison of these partitioning algorithms and cost-estimation policies on a large scale parallel
execution of XDEM running on the HPC platform of the University of Luxembourg. Finally, after
explaining the pros and cons of each partitioning algorithms and cost-estimation policies, we discuss
on the best choices to adopt depending on the simulation case.
Researchers ; Professionals
http://hdl.handle.net/10993/32810

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