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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  3/2018 - 1
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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: 1,210 | Views: 2,183

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 63] [SCOPUS Times Cited 65]


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[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 16]


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[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 56]


[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.
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[CrossRef]


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[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 5] [SCOPUS Times Cited 6]


[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 79] [SCOPUS Times Cited 90]


[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 5]


[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.
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 21]


[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.
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[17] P.-Y. Hong, L.-M. Huang, L.-S. Lin, C.-A. Lin, "Scalable multi-relaxation-time lattice Boltzmann simulations on multi-GPU cluster," Computers & Fluids, vol. 110, pp. 1-8, 2015.
[CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 34]


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[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 140]


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[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 12] [SCOPUS Times Cited 13]


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[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 73]


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[CrossRef] [Web of Science Times Cited 521] [SCOPUS Times Cited 613]




References Weight

Web of Science® Citations for all references: 10,290 TCR
SCOPUS® Citations for all references: 11,189 TCR

Web of Science® Average Citations per reference: 412 ACR
SCOPUS® Average Citations per reference: 448 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 2023-05-21 17:28 in 138 seconds.




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