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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Proceedings ArticleDOI
A MapReduce Style Framework for Computations on Trees
Abhinav Sarje,Srinivas Aluru +1 more
TL;DR: This paper presents a high-level framework for computations on tree structures, and demonstrates the applicability of the framework by solving two applications -- k-nearest neighbors and fast multipole method based simulations -- by merely using the framework in multiple ways.
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Collaborative accelerators for in-memory MapReduce on scale-up machines
Abraham Addisie,Valeria Bertacco +1 more
TL;DR: CASM is proposed, an architecture that equips each core in a CMP design with a dedicated instance of a specialized hardware unit (the CASM accelerators) that collaborate to manage the key-value data structure and minimize both on- and off-chip communication costs.
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Parallelization of searching and mining time series data using Dynamic Time Warping
Ahmed Shabib,Anish Narang,Chaitra Prasad Niddodi,Madhura Das,Rachita Pradeep,Varun Shenoy,Prafullata Auradkar,Vignesh Ts,Dinkar Sitaram +8 more
TL;DR: This paper considers 2 methods of parallelizing the UCR Dynamic Time Warping algorithm, a multi-core implementation, followed by a cluster implementation using Spark and shows how to compute distributed lower bounds efficiently in Spark and achieve nearly linear speedup with DTW in a Spark computation as well.
P4P: A Practical Framework for Privacy-Preserving Distributed Computation
TL;DR: P4P: A Practical Framework for Privacy-Preserving Distributed Computation is presented, which aims to provide a roadmap for the development of truly distributed systems.
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