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Blood Flow Arterial Network Simulation with the Implicit Parallelism Library SkelGIS

Authors :
Jose-Maria Fullana
Sébastien Limet
Hélène Coullon
Xiaofei Wang
Pierre-Yves Lagrée
Laboratoire d'Informatique Fondamentale d'Orléans (LIFO)
Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL)
Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)
Institut Jean le Rond d'Alembert (DALEMBERT)
Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)
Laboratoire de Chimie des Processus Biologiques (LCPB)
Université Pierre et Marie Curie - Paris 6 (UPMC)-Collège de France (CdF (institution))-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)
Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges
Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO)
Coullon, Hélène
Source :
ICCS, International Conference on Computational Science, International Conference on Computational Science, 2014, Oudonc, France. pp.102-112, ⟨10.1016/j.procs.2014.05.010⟩, ICCS 2014, ICCS 2014, Jun 2014, Cairns, Australia
Publication Year :
2014
Publisher :
Elsevier BV, 2014.

Abstract

Implicit parallelism computing is an active research domain of computer science. Most implicit parallelism solutions to solve partial differential equations, and scientific simulations, are based on the specificity of numerical methods, where the user has to call specific functions which embed parallelism. This paper presents the implicit parallel library SkelGIS which allows the user to freely write its numerical method in a sequential programming style in C++. This library relies on four concepts which are applied, in this paper, to the specific case of network simulations. SkelGIS is evaluated on a blood flow simulation in arterial networks. Benchmarks are first performed to compare the performance and the coding difficulty of two implementations of the simulation, one using SkelGIS, and one using OpenMP. Finally, the scalability of the SkelGIS implementation, on a cluster, is studied up to 1024 cores.

Details

ISSN :
18770509
Volume :
29
Database :
OpenAIRE
Journal :
Procedia Computer Science
Accession number :
edsair.doi.dedup.....8238acfccf35aebd7d7208776af1d742