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Lattice Boltzmann Method Implementation on Multiple Devices using OpenCLTEKIC, J. B.![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
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Author keywords
Lattice Boltzmann methods, multicore processing, scientific computing, parallel programming, parallel algorithms
References keywords
lattice(21), boltzmann(21), method(11), multi(8), simulations(6), flow(6), flows(5), computational(5), time(4), relaxation(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03001
Web of Science Accession Number: 000442420900001
SCOPUS ID: 85052088705
Abstract
Scientific computing community has been in close connection with high performance computing (HPC), which has been privilege of a limited group of scientists. Recently, with rapid development of Graphics Processing Units (GPUs), the parallel processing power of high performance computers has been brought up to every commodity desktop computer, reducing cost of scientific computations. In this paper, we develop a general purpose Lattice Boltzmann code that runs on commodity computer with multiple heterogeneous devices that support OpenCL specification. Different approaches to Lattice Boltzmann code implementations on commodity computer with multiple devices were explored. Simulation results for different code implementations on multiple devices have been compared to each other, to results obtained for single device implementation and with results from the literature. Simulation results for the commodity computer hardware platforms with multiple devices implementation have showed significant speed improvement compared to simulation implemented on single device. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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