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Computational performance of SequenceL coding of the lattice Boltzmann method for multi-particle flow simulations
- Source :
- Computer physics communications 213 (2017): 92–99. doi:10.1016/j.cpc.2016.12.012, info:cnr-pdr/source/autori:Basagaoglu, Hakan; Blount, Justin; Blount, Jarred; Nelson, Bryant; Succi, Sauro; Westhart, Phil M.; Harwell, John R./titolo:Computational performance of SequenceL coding of the lattice Boltzmann method for multi-particle flow simulations/doi:10.1016%2Fj.cpc.2016.12.012/rivista:Computer physics communications/anno:2017/pagina_da:92/pagina_a:99/intervallo_pagine:92–99/volume:213
- Publication Year :
- 2017
- Publisher :
- North-Holland, Amsterdam , Paesi Bassi, 2017.
-
Abstract
- This paper reports, for the first time, the computational performance of SequenceL for mesoscale simulations of large numbers of particles in a microfluidic device via the lattice-Boltzmann method. The performance of SequenceL simulations was assessed against the optimized serial and parallelized (via OpenMP directives) FORTRAN90 simulations. At present, OpenMP directives were not included in interparticle and particle-wall repulsive (steric) interaction calculations due to difficulties that arose from inter-iteration dependencies between consecutive iterations of the do-loops. SequenceL simulations, on the other hand, relied on built-in automatic parallelism. Under these conditions, numerical simulations revealed that the parallelized FORTRAN90 outran the performance of SequenceL by a factor of 2.5 or more when the number of particles was 100 or less. SequenceL, however, outran the performance of the parallelized FORTRAN90 by a factor of 1.3 when the number of particles was 300. Our results show that when the number of particles increased by 30-fold, the computational time of SequenceL simulations increased linearly by a factor of 1.5, as compared to a 3.2-fold increase in serial and a 7.7-fold increase in parallelized FORTRAN90 simulations. Considering SequenceL's efficient built-in parallelism that led to a relatively small increase in computational time with increased number of particles, it could be a promising programming language for computationally-efficient mesoscale simulations of large numbers of particles in microfluidic experiments. (C) 2016 Elsevier B.V. All rights reserved.
- Subjects :
- Particle number
Computer science
SequenceL
Lattice Boltzmann methods
General Physics and Astronomy
Parallel computing
Computational methods in fluid dynamics
01 natural sciences
010305 fluids & plasmas
Computational science
Hardware and Architecture
0103 physical sciences
Hydrodynamics
Particle flow
010306 general physics
Coding (social sciences)
Lattice-Boltzmann
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Computer physics communications 213 (2017): 92–99. doi:10.1016/j.cpc.2016.12.012, info:cnr-pdr/source/autori:Basagaoglu, Hakan; Blount, Justin; Blount, Jarred; Nelson, Bryant; Succi, Sauro; Westhart, Phil M.; Harwell, John R./titolo:Computational performance of SequenceL coding of the lattice Boltzmann method for multi-particle flow simulations/doi:10.1016%2Fj.cpc.2016.12.012/rivista:Computer physics communications/anno:2017/pagina_da:92/pagina_a:99/intervallo_pagine:92–99/volume:213
- Accession number :
- edsair.doi.dedup.....b9b233ec87d38700ff89c8ab13dd19bc
- Full Text :
- https://doi.org/10.1016/j.cpc.2016.12.012