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Optimizing the Forward Algorithm for Hidden Markov Model on IBM Roadrunner clustersSOIMAN, S.-I. , RUSU, I. , PENTIUC, S.-G.
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forward algorithm, hidden Markov models, multicore processing, parallel hybrid architectures, parallel programming, performance analysis
parallel(9), models(6), markov(6), hidden(6), cell(6), systems(5), ipdps(5), distributed(5), computing(5), recognition(4)
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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
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|
| T. F. Oliver, B. Schmidt, Y. Jakop, D. L. Maskell, "High Speed Biological Sequence Analysis With Hidden Markov Models on Reconfigurable Platforms", Information Technology in Biomedicine, IEEE Transactions on 13(5): 740-746. |
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]
 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 7]
 J. Nielsen, A. Sand, "Algorithms for a Parallel Implementation of Hidden Markov Models with a Small State Space", in Proc. IPDPS Workshops, IEEE 2011, pp.452-459.
[CrossRef] [SCOPUS Times Cited 3]
 X. Meng, Y. Ji, "Modern Computational Techniques for the HMMER Sequence Analysis", vol.2013, 13 pages, 2013.
 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 1]
 L. Yu, Y. Ukidave and D. Kaeli, "GPU-accelerated HMM for Speech Recognition", Workshop - Heterogeneous and Unconventional Cluster Architectures and Applications (HUCAA) September, 2014.
 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 5] [SCOPUS Times Cited 7]
 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 12]
 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 31]
 W. Lee, J. Kim, I. Lane, "GPU Accelerated Model Combination for Robust Speech Recognition and Keyword Search", GPU Technology Conference, March 2014
 T. Chen, R. Raghavan, J. N. Dale, E. Iwata, "Cell Broadband Engine Architecture and its first implementationA performance view", IBM Journal of Research and Development , vol.51, no.5, pp.559-572, 2007.
[CrossRef] [SCOPUS Times Cited 140]
 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 18] [SCOPUS Times Cited 24]
 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 2]
 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.
 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 4]
 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
 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]
 A. Arevalo, R.M. Matinata, M. Pandian, E. Peri, K. Ruby, F. Thomas, C. Almond: Programming for the Cell Broadband Engine. IBM Redbooks (2008)
 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]
 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 1]
 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]
 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 8264] [SCOPUS Times Cited 11360]
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