Parallel mining of closed sequential patterns
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Tolerating Dependences Between Large Speculative Threads Via Sub-Threads
Proceedings of the 33rd annual international symposium on Computer Architecture
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Toward terabyte pattern mining: an architecture-conscious solution
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Evaluating MapReduce for Multi-core and Multiprocessor Systems
HPCA '07 Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture
Output space sampling for graph patterns
Proceedings of the VLDB Endowment
Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware
Proceedings of the 7th ACM international conference on Computing frontiers
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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).