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JCR Impact Factor: 0.595
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  1/2012 - 9

Implementation of the Lattice Boltzmann Method on Heterogeneous Hardware and Platforms using OpenCL

TEKIC, P. M. See more information about TEKIC, P. M. on SCOPUS See more information about TEKIC, P. M. on IEEExplore See more information about TEKIC, P. M. on Web of Science, RADJENOVIC, J. B. See more information about  RADJENOVIC, J. B. on SCOPUS See more information about  RADJENOVIC, J. B. on SCOPUS See more information about RADJENOVIC, J. B. 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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (662 KB) | Citation | Downloads: 896 | Views: 2,903

Author keywords
GPU, Java, lattice Boltzmann method, many-core, OpenC

References keywords
lattice(17), boltzmann(15), performance(5), fluid(5), flow(5), simulation(4), parallel(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-02-28
Volume 12, Issue 1, Year 2012, On page(s): 51 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.01009
Web of Science Accession Number: 000301075000009
SCOPUS ID: 84860735612

Abstract
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Full text preview
The Lattice Boltzmann method (LBM) has become an alternative method for computational fluid dynamics with a wide range of applications. Besides its numerical stability and accuracy, one of the major advantages of LBM is its relatively easy parallelization and, hence, it is especially well fitted to many-core hardware as graphics processing units (GPU). The majority of work concerning LBM implementation on GPU's has used the CUDA programming model, supported exclusively by NVIDIA. Recently, the open standard for parallel programming of heterogeneous systems (OpenCL) has been introduced. OpenCL standard matures and is supported on processors from most vendors. In this paper, we make use of the OpenCL framework for the lattice Boltzmann method simulation, using hardware accelerators - AMD ATI Radeon GPU, AMD Dual-Core CPU and NVIDIA GeForce GPU's. Application has been developed using a combination of Java and OpenCL programming languages. Java bindings for OpenCL have been utilized. This approach offers the benefits of hardware and operating system independence, as well as speeding up of lattice Boltzmann algorithm. It has been showed that the developed lattice Boltzmann source code can be executed without modification on all of the used hardware accelerators. Performance results have been presented and compared for the hardware accelerators that have been utilized.


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

[1] G. Khanna and J. McKennon, "Numerical modeling of gravitational wave sources accelerated by OpenCL," Computer Physics Communications, vol. 181 pp. 1605-1611, 2010.
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 19]


[2] M. J. Harvey and G. D. Fabritiis, "Swan: A tool for porting CUDA programs to OpenCL," Computer Physics Communications.

[3] S. Succi, The Lattice Boltzman Equation for Fluid Dynamics and Beyond. Oxford: Oxford University Press, 2001.

[4] D. Yu, R. Mei, L.-S. Luo, and W. Shyy, "Viscous low computations with the method of lattice Boltzmann equation," Progress in Aerospace Sciences, vol. 39, pp. 329-367, 2003.
[CrossRef] [Web of Science Times Cited 464] [SCOPUS Times Cited 567]


[5] L.-S. Luo, "The lattice-gas and lattice Boltzmann methods: Past, present, and future," in Proc Int Conf Appl Comput Fluid Dyn, Beijing, 2000, pp. 52-83.

[6] M. C. Sukop and D. T. J. Thorne, Lattice Boltzmann Modeling: An Introduction for Geoscientists and Engineers. Berlin: Springer, 2007.

[7] S. Williams, J. Carter, L. Oliker, J. Shalf, and K. A. Yelick, "Lattice Boltzmann simulation optimization on leading multicore platforms," in IEEE International Symposium on Parallel and Distributed Processing, 2008, pp. 1-14.
[CrossRef] [SCOPUS Times Cited 47]


[8] T. Pohl, et al., "Performance Evaluation of Parallel Large-Scale Lattice Boltzmann Applications on Three Supercomputing Architectures," presented at the Proceedings of the 2004 ACM/IEEE conference on Supercomputing, 2004.
[CrossRef] [SCOPUS Times Cited 16]


[9] G. Wellein, T. Zeiser, G. Hager, and S. Donath, "On the single processor performance of simple lattice Boltzmann kernels," Computers & Fluids, vol. 35, pp. 910-919.
[CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 127]


[10] D. Vidal, R. Roy, and F. Bertrand, "A parallel workload balanced and memory efficient lattice-Boltzmann algorithm," Computers & Fluids, vol. 39, pp. 1411-1423, 2010.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[11] M. Bernaschi, M. Fatica, S. Melchionna, S. Succi, and E. Kaxiras, "A flexible high-performance Lattice Boltzmann GPU code for the simulations of fluid flows in complex geometries," Concurr. Comput. : Pract. Exper., vol. 22, pp. 1-14, 2010.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 60]


[12] K. R. Tubbs and F. T. C. Tsai, "GPU accelerated lattice Boltzmann model for shallow water flow and mass transport," International Journal for Numerical Methods in Engineering, vol. 86, pp. 316-334, 2011.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 14]


[13] W. Li, X. Wei, and A. Kaufman, "Implementing Lattice Boltzmann Computation on Graphics Hardware," Visual Computer, vol. 19, pp. 444-456, 2003.

[14] J. Tolke and M. Krafczyk, "TeraFLOP computing on a desktop PC with GPUs for 3D CFD," International Journal of Computational Fluid Dynamics, vol. 22, pp. 443-456, 2008.
[CrossRef] [Web of Science Times Cited 133] [SCOPUS Times Cited 164]


[15] F. Kuznik, C. Obrecht, G. Rusaouen, and J.-J. Roux, "LBM based flow simulation using GPU computing processor," Computers & Mathematics with Applications, vol. 59, pp. 2380-2392, 2010.
[CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 83]


[16] J. Habich, T. Zeiser, G. Hager, and G. Wellein, "Performance analysis and optimization strategies for a D3Q19 lattice Boltzmann kernel on nVIDIA GPUs using CUDA," Advances in Engineering Software, vol. 42, pp. 266-272, 2011.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 15]


[17] C. Obrecht, F. Kuznik, B. Tourancheau, and J.-J. Roux, "A new approach to the lattice Boltzmann method for graphics processing units," Computers & Mathematics with Applications, vol. In Press, Corrected Proof.

[18] W. Xian and A. Takayuki, "Multi-GPU performance of incompressible flow computation by lattice Boltzmann method on GPU cluster," Parallel Computing, vol. In Press, Corrected Proof.

[19] JOCL Library, [Online] Available: Temporary on-line reference link removed - see the PDF document

[20] P. M. Tekic, J. B. Radenovic, N. L. Lukic, and S. S. Popovic, "Lattice Boltzmann simulation of two-sided lid-driven flow in a staggered cavity," International Journal of Computational Fluid Dynamics, vol. 24, pp. 383-390, 2010.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 7]


[21] D. V. Patil, K. N. Lakshmisha, and B. Rogg, "Lattice Boltzmann simulation of lid-driven flow in deep cavities," Computers & Fluids, vol. 35, pp. 1116-1125, 2006.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 48]


References Weight

Web of Science® Citations for all references: 908 TCR
SCOPUS® Citations for all references: 1,175 TCR

Web of Science® Average Citations per reference: 43 ACR
SCOPUS® Average Citations per reference: 56 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 2017-09-23 10:14 in 89 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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