Reference : Hybrid MPI+OpenMP Implementation of eXtended Discrete Element Method
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/36374
Hybrid MPI+OpenMP Implementation of eXtended Discrete Element Method
English
Mainassara Chekaraou, Abdoul Wahid mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
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]
Varrette, Sébastien mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Peters, Bernhard mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Sep-2018
Proc. of the 9th Workshop on Applications for Multi-Core Architectures (WAMCA'18), part of 30th Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD 2018)
IEEE Computer Society
Yes
No
International
Lyon, France
9th Workshop on Applications for Multi-Core Architectures (WAMCA'18), part of 30th Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD 2018)
sept 24-27, 2018
Lyon
France
[en] The Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that ex- tends classical Discrete Element Method (DEM) (which simulates the motion of granular material), by additional properties such as the chemical composition, thermodynamic state, stress/strain for each particle. It has been applied successfully to numerous industries involving the processing of granular materials such as sand, rock, wood or coke [16], [17].
In this context, computational simulation with (X)DEM has become a more and more essential tool for researchers and scientific engineers to set up and explore their experimental processes. However, increasing the size or the accuracy of a model requires the use of High Performance Computing (HPC) platforms over a parallelized implementation to accommodate the growing needs in terms of memory and computation time. In practice, such a parallelization is traditionally obtained using either MPI (distributed memory computing), OpenMP (shared memory computing) or hybrid approaches combining both of them.
In this paper, we present the results of our effort to implement an OpenMP version of XDEM allowing hybrid MPI+OpenMP simulations (XDEM being already parallelized with MPI). Far from the basic OpenMP paradigm and recommendations (which simply summarizes by decorating the main computation loops with a set of OpenMP pragma), the OpenMP parallelization of XDEM required a fundamental code re-factoring and careful tuning in order to reach good performance. There are two main reasons for those difficulties. Firstly, XDEM is a legacy code devel- oped for more than 10 years, initially focused on accuracy rather than performance. Secondly, the particles in a DEM simulation are highly dynamic: they can be added, deleted and interaction relations can change at any timestep of the simulation. Thus this article details the multiple layers of optimization applied, such as a deep data structure profiling and reorganization, the usage of fast multithreaded memory allocators and of advanced process/thread-to-core pinning techniques. Experimental results evaluate the benefit of each optimization individually and validate the implementation using a real-world application executed on the HPC platform of the University of Luxembourg. Finally, we present our Hybrid MPI+OpenMP results with a 15%-20% performance gain and how it overcomes scalability limits (by increasing the number of compute cores without dropping of performances) of XDEM-based pure MPI simulations.
University of Luxembourg: High Performance Computing - ULHPC
Luxdem.LSDEM
http://hdl.handle.net/10993/36374
https://graal.ens-lyon.fr/sbac-pad/

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