IEICE TRANSACTIONS on Information
Efficient Parallel Learning of Hidden Markov Chain Models on SMPs
Summary :
Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.6 pp.1330-1342
- Publication Date
- 2010/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.1330
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Info-Plosion)
- Category
Authors
Keyword
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Lei LI, Bin FU, Christos FALOUTSOS, "Efficient Parallel Learning of Hidden Markov Chain Models on SMPs" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 6, pp. 1330-1342, June 2010, doi: 10.1587/transinf.E93.D.1330.
Abstract: Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1330/_p
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@ARTICLE{e93-d_6_1330,
author={Lei LI, Bin FU, Christos FALOUTSOS, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Parallel Learning of Hidden Markov Chain Models on SMPs},
year={2010},
volume={E93-D},
number={6},
pages={1330-1342},
abstract={Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.},
keywords={},
doi={10.1587/transinf.E93.D.1330},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Efficient Parallel Learning of Hidden Markov Chain Models on SMPs
T2 - IEICE TRANSACTIONS on Information
SP - 1330
EP - 1342
AU - Lei LI
AU - Bin FU
AU - Christos FALOUTSOS
PY - 2010
DO - 10.1587/transinf.E93.D.1330
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2010
AB - Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.
ER -
English
