Open AccessProceedings Article
Map-Reduce for Machine Learning on Multicore
Cheng-Tao Chu,Sang K. Kim,Yi-an Lin,Yuanyuan Yu,Gary Bradski,Kunle Olukotun,Andrew Y. Ng +6 more
- Vol. 19, pp 281-288
TLDR
This work shows that algorithms that fit the Statistical Query model can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers and shows basically linear speedup with an increasing number of processors.Abstract:
We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers. We adapt Google's map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.read more
Citations
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A decentralized training algorithm for Echo State Networks in distributed big data applications
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Simulation of database-valued markov chains using SimSQL
TL;DR: The SimSQL system, which allows for SQLbased specification, simulation, and querying of database-valued Markov chains, i.e., chains whose value at any time step comprises the contents of an entire database, is described.
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Hierarchical attribute reduction algorithms for big data using MapReduce
TL;DR: H hierarchical attribute reduction algorithms are proposed in data and task parallel using MapReduce and Experimental results demonstrate that the proposed algorithms can scale well and efficiently process big data.
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Hybrid cloud and cluster computing paradigms for life science applications
Judy Qiu,Jaliya Ekanayake,Thilina Gunarathne,Jong Youl Choi,Seung-Hee Bae,Hui Li,Bingjing Zhang,Tak-Lon Wu,Yang Ruan,Saliya Ekanayake,Adam Hughes,Geoffrey C. Fox +11 more
TL;DR: The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications.
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Parallel Spectral Clustering
TL;DR: In this article, the authors proposed to parallelize both memory use and computation on distributed computers to solve the scalability problem of spectral clustering on large datasets, and demonstrated that their parallel algorithm can effectively alleviate scalability problems.
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