Cut-and-stitch

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Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps

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International Conference on Knowledge Discovery and Data Mining archive
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Las Vegas, Nevada, USA

SESSION: Research papers table of contents

Pages 471-479

Year of Publication: 2008

ISBN:978-1-60558-193-4

Authors

Lei Li	Carnegie Mellon University, Pittsburgh, PA, USA
Wenjie Fu	Carnegie Mellon University, Pittsburgh, PA, USA
Fan Guo	Carnegie Mellon University, Pittsburgh, PA, USA
Todd C. Mowry	Carnegie Mellon University, Pittsburgh, PA, USA
Christos Faloutsos	Carnegie Mellon University, Pittsburgh, PA, USA

Sponsors

ACM: Association for Computing Machinery
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 13, Downloads (12 Months): 204, Citation Count: 0

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ABSTRACT

Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up data mining algorithms. Specifically, we present a parallel algorithm for approximate learning of Linear Dynamical Systems (LDS), also known as Kalman Filters (KF). LDSs are widely used in time series analysis such as motion capture modeling, visual tracking etc. We propose Cut-And-Stitch (CAS), a novel method to handle the data dependencies from the chain structure of hidden variables in LDS, so as to parallelize the EM-based parameter learning algorithm. We implement the algorithm using OpenMP on both a supercomputer 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 serial version. In addition, Cut-And-Stitch can be generalized to other models with similar linear structures such as Hidden Markov Models (HMM) and Switching Kalman Filters (SKF).

REFERENCES

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