Speeding up the MATLAB complex networks package using graphic processors

doi:10.1088/1674-1056/20/9/098901

Abstract The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units (GPU). This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3×. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.

Keywords: complex networks graphic processors unit MATLAB Jacket Toolbox

Received: 18 March 2011
Revised: 04 May 2011
Accepted manuscript online:

PACS:	89.75.Hc	(Networks and genealogical trees)
	87.23.Ge	(Dynamics of social systems)
	89.20.Hh	(World Wide Web, Internet)
	89.75.-k	(Complex systems)

Cite this article:

Zhang Bai-Da(张百达), Tang Yu-Hua(唐玉华), Wu Jun-Jie(吴俊杰), and Li Xin(李鑫) Speeding up the MATLAB complex networks package using graphic processors 2011 Chin. Phys. B 20 098901

[1]	Watts D J and Strogatz S H 1998 Nature 393 440
[2]	Barab A L and Albert R 1999 Science 286 509
[3]	Newman M E and Girvan M 2004 Phys. Rev. E 69 26113
[4]	Barrat A, Barthelemy M and Vespignani A 2004 Phys. Rev. Lett. 22 228701
[5]	Albert R and Barab A L 2002 Rev. Mod. Phys. 74 47
[6]	Newman M E 2003 SIAM Rev. 45 167
[7]	Cui D, Gao Z Y and Zhao X M 2008 Chin. Phys. B 17 1703
[8]	Zhang C X, Li H and Lin P 2008 Chin. Phys. B 17 4458
[9]	NVIDIA 2003 SIAM Review 45 167
[10]	The AccelerEyes 2011 Jacket User Guide http://www.accelereyes.com
[11]	Nickolls J 2010 Stanford CS 193G Lecture 15 56 72
[12]	Hennessy J L, Patterson D A and Goldberg D 2003 Computer Architecture: A Quantitative Approach 3rd edn. (San Francisco: Morgan Kaufmann) pp. 128—132
[13]	Newman M 2002 Phys. Rev. Lett. 89 208701
[14]	Pei W D, Chen Z Q and Yuan Z Z 2008 Chin. Phys. B 17 373
[15]	Rodrigues F, Travieso G and Boas V 2007 Adv. Phys. 56 167
[16]	Shen Y 2011 Chin. Phys. B 20 511
[17]	Tang Y, Wong W K, Fang J A and Miao Q Y 2011 Chin. Phys. B 20 513
[18]	Wang S J, Wu Z X, Dong H R and Chen G R 2011 Chin. Phys. B 20 903
[19]	Ryoo S, Rodrigues C I, Stone S S, Stratton J A, Ueng S Z, Baghsorkhi S S and Hwu W W 2008 Journal of Parallel and Distributed Computing 68 1389
[20]	Ryoo S, Rodrigues C I, Stone S S, Baghsorkhi S S, Ueng S Z, Stratton J A and Hwu W W 2008 newblock Program Optimization Space Pruning for a Multithreaded GPU
[21]	Shane R, Ryoo S, Rodrigues C I, Baghsorkhi S S, Stone S S, Kirk D B, et al. 2007 Parallel Comput. 33 663
[22]	Messmer P, Mullowney P J and Granger B E 2008 Computing in Science and Engineering 10 70
[23]	The GP-you group 2011 GPUmat User Guide
[24]	Choy R and Edelman A 2005 Proc. IEEE 93 331
[25]	Kong J, Dimitrov M, Yang Y, Liyanage J, Cao L, Staples J, Mantor M and Zhou H 2010 Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units pp. 78—85
[26]	Szafaryn L G, Skadron K and Saucerman J J 2009 Biomedicine in Computing: Systems, Architectures, and Circuits 32 1
[27]	Moore N and Leeser M 2009 High Performance Embedded Computing Workshop
[28]	Brodtkorb A R 2008 Scientific Computing on Heterogeneous Architectures 12 109
[29]	Fatica M and Jeong W K 2007 Proceedings of the Eleventh Annual High Performance Embedded Computing Workshop, Lexington, Massachusetts 2007 pp. 18—20

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