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JCR Impact Factor: 0.699
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Issues per year: 4
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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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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  2/2015 - 13

Optimizing the Forward Algorithm for Hidden Markov Model on IBM Roadrunner clusters

SOIMAN, S.-I. See more information about SOIMAN, S.-I. on SCOPUS See more information about SOIMAN, S.-I. on IEEExplore See more information about SOIMAN, S.-I. on Web of Science, RUSU, I. See more information about  RUSU, I. on SCOPUS See more information about  RUSU, I. on SCOPUS See more information about RUSU, I. on Web of Science, PENTIUC, S.-G. See more information about PENTIUC, S.-G. on SCOPUS See more information about PENTIUC, S.-G. on SCOPUS See more information about PENTIUC, S.-G. on Web of Science
Click to see author's profile in 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 (1,006 KB) | Citation | Downloads: 294 | Views: 1,912

Author keywords
forward algorithm, hidden Markov models, multicore processing, parallel hybrid architectures, parallel programming, performance analysis

References keywords
parallel(9), models(6), markov(6), hidden(6), cell(6), systems(5), ipdps(5), distributed(5), computing(5), recognition(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-05-31
Volume 15, Issue 2, Year 2015, On page(s): 103 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.02013
Web of Science Accession Number: 000356808900013
SCOPUS ID: 84979846307

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In this paper we present a parallel solution of the Forward Algorithm for Hidden Markov Models. The Forward algorithm compute a probability of a hidden state from Markov model at a certain time, this process being recursively. The whole process requires large computational resources for those models with a large number of states and long observation sequences. Our solution in order to reduce the computational time is a multilevel parallelization of Forward algorithm. Two types of cores were used in our implementation, for each level of parallelization, cores that are graved on the same chip of PowerXCell8i processor. This hybrid architecture of processors permitted us to obtain a speedup factor over 40 relative to the sequential algorithm for a model with 24 states and 25 millions of observable symbols. Experimental results showed that the parallel Forward algorithm can evaluate the probability of an observation sequence on a hidden Markov model 40 times faster than the classic one does. Based on the performance obtained, we demonstrate the applicability of this parallel implementation of Forward algorithm in complex problems such as large vocabulary speech recognition.

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

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[2] A. Sand, Pedersen, C. N. S. Pedersen, T. Mailund, A. T. Brask, "HMMlib: A C++ Library for General Hidden Markov Models Exploiting Modern CPUs", 2010 Ninth International Workshop on Parallel and Distributed Methods in Verification, and Second International Workshop on High Performance Computational Systems Biology, IEEE 2010, pp. 126 - 134, 2010.
[CrossRef] [SCOPUS Times Cited 10]

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[4] X. Meng, Y. Ji, "Modern Computational Techniques for the HMMER Sequence Analysis", vol.2013, 13 pages, 2013.

[5] S. Gorgunoglu, I. M. Orak, A. Cavusoglu, M. Gok, "Examination of Speed Contribution of Parallelization for Several Fingerprint Pre-Processing Algorithms," Advances in Electrical and Computer Engineering, vol. 14, no. 2, pp. 3-8, 2014,
[CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]

[6] L. Yu, Y. Ukidave and D. Kaeli, "GPU-accelerated HMM for Speech Recognition", Workshop - Heterogeneous and Unconventional Cluster Architectures and Applications (HUCAA) September, 2014.

[7] J. Li, S. Chen, Y. Li, "The fast evaluation of hidden Markov models on GPU," Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on , vol.4, no., pp.426,430, 20-22 Nov. 2009.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 10]

[8] D. Zhihui, Y. Zhaoming, D.A. Bader, "A tile-based parallel Viterbi algorithm for biological sequence alignment on GPU with CUDA," Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on , vol., no., pp.1,8, 19-23 April 2010.
[CrossRef] [SCOPUS Times Cited 16]

[9] J.P. Walters, V. Balu, S. Kompalli, V. Chaudhary, "Evaluating the use of GPUs in liver image segmentation and HMMER database searches," Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on , vol., no., pp.1,12, 23-29 May 2009.
[CrossRef] [SCOPUS Times Cited 39]

[10] W. Lee, J. Kim, I. Lane, "GPU Accelerated Model Combination for Robust Speech Recognition and Keyword Search", GPU Technology Conference, March 2014

[11] T. Chen, R. Raghavan, J. N. Dale, E. Iwata, "Cell Broadband Engine Architecture and its first implementation—A performance view", IBM Journal of Research and Development , vol.51, no.5, pp.559-572, 2007.
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[12] V. Sachdeva, M. Kistler, E. Speight, T.-H. K. Tzeng, "Exploring the viability of the Cell Broadband Engine for bioinformatics applications, " Parallel Computing, vol. 34, no. 11, pp. 616-626, 2008.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 26]

[13] S.-I. Soiman, I. Rusu, S.-G. Pentiuc, "A parallel accelerated approach of HMM Forward Algorithm for IBM Roadrunner clusters", Proceedings of the 12th Int. Conf. on Development and Appl. Systems, May 2014, pp. 184-188. .
[CrossRef] [SCOPUS Times Cited 3]

[14] S.-I. Soiman, I. Rusu, S.-G. Pentiuc, " Multilevel Parallelized Forward Algorithm for Hidden Markov Models on IBM Roadrunner Cluster", Proceedings of the 20th Int. Conf. on Control Systems and Computer Science, May 2015.

[15] F. Blagojevic, A. Stamatakis, C. D. Antonopoulos, D. S. Nikolopoulos, "RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell Broadband Engine, " Parallel and Distributed Processing Symposium, IEEE International, pp. 1-10, 2007.
[CrossRef] [SCOPUS Times Cited 41]

[16] GRIDNORD Project. High Performance Computing Laboratory of the Faculty of Electrical Engineering and Computer Science, Suceava, Romania, 2012, [Online] Available: Temporary on-line reference link removed - see the PDF document

[17] A. L. Varbanescu, H. Sips, K.A. Ross, Q. Liu, A. Natsev, J.R. Smith and L.K. Liu, "Evaluating application mapping scenarios on the Cell/B.E, " Concurrency and Computation: Practice and Experience, 21, pp. 85-100, 2009.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]

[18] A. Arevalo, R.M. Matinata, M. Pandian, E. Peri, K. Ruby, F. Thomas, C. Almond: Programming for the Cell Broadband Engine. IBM Redbooks (2008)

[19] C. A. Tanase, V. G. Gaitan, "Threads Pipelining on the CellBE Systems", Advances in Electrical and Computer Engineering, vol. 13, no. 3, pp. 121-126, 2013.
[CrossRef] [Full Text] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]

[20] S.-G. Pentiuc, I. Ungurean, "Multilevel Parallelization of Unsupervised Learning Algorithms in Pattern Recognition on a Roadrunner Architecture ", Intelligent Distributed Computing V, vol. 382, pp.71 - 80, 2011.
[CrossRef] [SCOPUS Times Cited 4]

[21] I. Ungurean, V.-G. Gaitan, N.-C. Gaitan, "Intensive computing on a large data volume with a short-vector single instruction multiple data processor," Computers & Digital Techniques, IET, vol.8, no.5, pp.219-228, 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]

[22] L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition", Proceedings of IEEE, Vol. 77, pp. 257-285, 1989.
[CrossRef] [Web of Science Times Cited 9958] [SCOPUS Times Cited 13475]

References Weight

Web of Science® Citations for all references: 10,107 TCR
SCOPUS® Citations for all references: 13,807 TCR

Web of Science® Average Citations per reference: 439 ACR
SCOPUS® Average Citations per reference: 600 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 2019-05-20 08:00 in 116 seconds.

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