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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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Lattice Boltzmann Method Implementation on Multiple Devices using OpenCL

TEKIC, J. B. See more information about TEKIC, J. B. on SCOPUS See more information about TEKIC, J. B. on IEEExplore See more information about TEKIC, J. B. on Web of Science, TEKIC, P. M. See more information about  TEKIC, P. M. on SCOPUS See more information about  TEKIC, P. M. on SCOPUS See more information about TEKIC, P. M. on Web of Science, RACKOVIC, M. See more information about RACKOVIC, M. on SCOPUS See more information about RACKOVIC, M. on SCOPUS See more information about RACKOVIC, M. on Web of Science
 
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Download PDF pdficon (1,197 KB) | Citation | Downloads: 819 | Views: 1,639

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
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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.


References | Cited By  «-- Click to see who has cited this paper

[1] W. Shi, W. Shyy, R. Mei, "Finite-difference-based lattice Boltzmann method for inviscid compressible flows," Numerical Heat Transfer, Part B: Fundamentals, vol. 40, no. 1, pp. 1-21, 2001.
[CrossRef] [Web of Science Times Cited 60] [SCOPUS Times Cited 61]


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[CrossRef] [Web of Science Times Cited 238] [SCOPUS Times Cited 272]


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[CrossRef] [Web of Science Times Cited 195]


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[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 11]


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[CrossRef] [Web of Science Times Cited 109] [SCOPUS Times Cited 119]


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[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 51]


[7] S. Tomov, M. McGuigan, R. Bennett, G. Smith, J. Spiletic, "Benchmarking and implementation of probability-based simulations on programmable graphics cards," Computers & Graphics, vol. 29, no. 1, pp. 71-80, 2005.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 39]


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[CrossRef] [Web of Science Times Cited 104] [SCOPUS Times Cited 141]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 1056] [SCOPUS Times Cited 1124]


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[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 1]


[12] P. M. Tekic, J. B. Radjenovic, M. Rackovic, "Implementation of the Lattice Boltzmann Method on Heterogeneous Hardware and Platforms using OpenCL," Advances in Electrical and Computer Engineering, vol. 12, no. 1, pp. 51-56, 2012.
[CrossRef] [Full Text] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[13] C. Obrecht, F. Kuznik, B. Tourancheau, J.-J. Roux, "Multi-GPU implementation of the lattice Boltzmann method," Computers & Mathematics with Applications, vol. 65, no. 2, pp. 252-261, 2013.
[CrossRef] [Web of Science Times Cited 67] [SCOPUS Times Cited 74]


[14] H.-W. Chang, P.-Y. Hong, L.-S. Lin, C.-A. Lin, "Simulations of Three-dimensional Cavity Flows with Multi Relaxation Time Lattice Boltzmann Method and Graphic Processing Units," Procedia Engineering, vol. 61, pp. 94-99, 2013.
[CrossRef] [SCOPUS Times Cited 4]


[15] H.-W. Chang, P.-Y. Hong, L.-S. Lin, C.-A. Lin, "Simulations of flow instability in three dimensional deep cavities with multi relaxation time lattice Boltzmann method on graphic processing units," Computers & Fluids, vol. 88, pp. 866-871, 2013.
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[16] C. Huang, B. Shi, N. He, Z. Chai, "Implementation of Multi-GPU Based Lattice Boltzmann Method for Flow Through Porous Media," Advances in Applied Mathematics and Mechanics, vol. 7, no. 1, pp. 1-12, 2015.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 18]


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[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 26]


[18] W. Xian, A. Takayuki, "Multi-GPU performance of incompressible flow computation by lattice Boltzmann method on GPU cluster," Parallel Computing, vol. 37, no. 9, pp. 521-535, 2011.
[CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 112]


[19] B. Massimo, F. Massimiliano, M. Simone, S. Sauro, K. Efthimios, "A flexible high-performance Lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries,"Concurrency and Computation: Practice and Experience, vol. 22, no. 1, pp. 1-14, 2010.
[CrossRef]


[20] E. Calore, S. F. Schifano, R. Tripiccione, "A Portable OpenCL Lattice Boltzmann Code for Multi- and Many-core Processor Architectures," Procedia Computer Science, vol. 29, pp. 40-49, 2014.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]


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[CrossRef] [SCOPUS Times Cited 5876]


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[CrossRef] [Web of Science Times Cited 1137] [SCOPUS Times Cited 1288]


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[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 66]


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[CrossRef] [Web of Science Times Cited 492] [SCOPUS Times Cited 576]




References Weight

Web of Science® Citations for all references: 3,766 TCR
SCOPUS® Citations for all references: 9,893 TCR

Web of Science® Average Citations per reference: 151 ACR
SCOPUS® Average Citations per reference: 396 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-09-17 03:57 in 237 seconds.




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