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
More filters
Proceedings ArticleDOI
Towards an OSGi Based Pervasive Cloud Infrastructure
TL;DR: This paper proposes an OSGi based pervasive cloud infrastructure which can make use both of the cloud computing capabilities and component flexibilities from OSGi, and evaluated the OSGi-PC in terms of performance and power consumption to show its usability.
Proceedings ArticleDOI
C-Cube: Elastic continuous clustering in the cloud
TL;DR: This paper proposes C-Cube, the first elastic approach to continuous streaming clustering, which is effective (in that it provides quality guarantees on the clustering results), effective, efficient, and generally applicable to a large class of clustering criteria.
Proceedings ArticleDOI
Parallel Simultaneous Co-clustering and Learning with Map-Reduce
TL;DR: This paper describes a general framework based on Simultaneous CO-clustering And Learning (SCOAL), which applies a divide-and-conquer approach to data analysis and shows that the main elements of the SCOAL algorithm can be effectively parallelized using the Map-Reduce framework.
Journal ArticleDOI
Performance characterization and analysis for Hadoop K-means iteration
TL;DR: A performance estimation model is proposed that estimates performance for Hadoop K-means iterations by modeling different processor micro-architecture parameters and comparing performance using Intel and AMD processors.
Proceedings ArticleDOI
Scaling the iHMM: Parallelization versus Hadoop
TL;DR: This paper compares parallel and distributed implementations of an iterative, Gibbs sampling, machine learning algorithm that is applied to learn part-of-speech tags from newswire text in an unsupervised fashion and focuses on runtime performance.
References
More filters
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI
Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.