RankMap: A Framework for Distributed Learning From Dense Data Sets
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization c...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems Jg. 29; H. 7; S. 2717 - 2730 |
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| Sprache: | Englisch |
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United States
IEEE
01.07.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
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| Abstract | This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications. To demonstrate the utility of RankMap, we solve sparse recovery and power iteration problems on various real-world data sets with up to 1.8 billion nonzeros. Our evaluations are performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores. The results demonstrate up to two orders of magnitude improvements in memory usage, execution speed, and bandwidth compared with the best reported prior work, while achieving the same level of learning accuracy. |
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| AbstractList | This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications. To demonstrate the utility of RankMap, we solve sparse recovery and power iteration problems on various real-world data sets with up to 1.8 billion nonzeros. Our evaluations are performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores. The results demonstrate up to two orders of magnitude improvements in memory usage, execution speed, and bandwidth compared with the best reported prior work, while achieving the same level of learning accuracy.This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications. To demonstrate the utility of RankMap, we solve sparse recovery and power iteration problems on various real-world data sets with up to 1.8 billion nonzeros. Our evaluations are performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores. The results demonstrate up to two orders of magnitude improvements in memory usage, execution speed, and bandwidth compared with the best reported prior work, while achieving the same level of learning accuracy. This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications. To demonstrate the utility of RankMap, we solve sparse recovery and power iteration problems on various real-world data sets with up to 1.8 billion nonzeros. Our evaluations are performed on Amazon EC2 and IBM iDataPlex servers using up to 244 cores. The results demonstrate up to two orders of magnitude improvements in memory usage, execution speed, and bandwidth compared with the best reported prior work, while achieving the same level of learning accuracy. |
| Author | Dyer, Eva L. Koushanfar, Farinaz Baraniuk, Richard Mirhoseini, Azalia Songhori, Ebrahim M. |
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| References | ref13 ref15 ref14 guennebaud (ref27) 2015 ref11 zaharia (ref49) 2012 drineas (ref16) 2005; 6 journée (ref30) 2010; 11 ref19 starck (ref45) 2002; 11 (ref1) 2014 (ref3) 0 ref51 ref46 low (ref34) 2010 ref47 liu (ref33) 2015; 16 ref41 recht (ref42) 2011 nodelman (ref39) 2012 dyer (ref18) 2013; 14 gittens (ref24) 2013 ref8 (ref2) 0 ref7 davis (ref12) 1994; 33 ref9 rubinstein (ref43) 2008; 40 ref4 ref6 ref5 ref35 ref37 ref36 ref31 ref32 pham (ref40) 2015 dyer (ref17) 2015 fine (ref20) 2002; 2 cortes (ref10) 2010 ref23 ref22 ref21 ref28 ref29 zaharia (ref50) 2010 montgomery (ref38) 2015 gonzalez (ref25) 2012 rudi (ref44) 2015 gray (ref26) 2001 yedidia (ref48) 2000 |
| References_xml | – volume: 11 start-page: 517 year: 2010 ident: ref30 article-title: Generalized power method for sparse principal component analysis publication-title: J Mach Learn Res – ident: ref36 doi: 10.1145/2461912.2461914 – ident: ref37 doi: 10.1137/120866580 – ident: ref5 doi: 10.1109/TIT.2010.2040894 – volume: 11 start-page: 670 year: 2002 ident: ref45 article-title: The curvelet transform for image denoising publication-title: IEEE Trans Electron Packag Manuf – ident: ref29 doi: 10.2307/1267351 – ident: ref22 doi: 10.1109/34.927464 – ident: ref51 doi: 10.1198/106186006X113430 – ident: ref8 doi: 10.1137/S1064827596304058 – ident: ref23 doi: 10.1109/ICDE.2011.5767930 – start-page: 753 year: 2015 ident: ref40 article-title: Robust sketching for multiple square-root LASSO problems publication-title: Proc AISTATS – volume: 6 start-page: 2153 year: 2005 ident: ref16 article-title: On the Nyström method for approximating a Gram matrix for improved kernel-based learning publication-title: J Mach Learn Res – start-page: 17 year: 2012 ident: ref25 article-title: PowerGraph: Distributed graph-parallel computation on natural graphs publication-title: Proc OSDI – volume: 2 start-page: 243 year: 2002 ident: ref20 article-title: Efficient SVM training using low-rank kernel representations publication-title: J Mach Learn Res – ident: ref11 doi: 10.1002/cpa.20042 – year: 2012 ident: ref39 publication-title: Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks – ident: ref9 doi: 10.1137/S1064827596304010 – ident: ref7 doi: 10.1007/s00041-008-9045-x – start-page: 693 year: 2011 ident: ref42 article-title: HOGWILD: A lock-free approach to parallelizing stochastic gradient descent publication-title: Proc NIPS – ident: ref31 doi: 10.1109/ICCV.2001.937679 – ident: ref28 doi: 10.1109/ICCV.2011.6126254 – ident: ref19 doi: 10.1109/TPAMI.2013.57 – ident: ref21 doi: 10.1109/TPAMI.2004.1262185 – start-page: 521 year: 2001 ident: ref26 article-title: 'N-body' problems in statistical learning publication-title: Proc NIPS – ident: ref13 doi: 10.1145/1327452.1327492 – ident: ref35 doi: 10.1145/1807167.1807184 – year: 2015 ident: ref17 publication-title: Self-Expressive Decompositions for Matrix Approximation and Clustering – ident: ref6 doi: 10.1137/080716542 – ident: ref41 doi: 10.1109/TPAMI.2002.1039204 – ident: ref14 doi: 10.1145/1109557.1109681 – volume: 33 start-page: 2183 year: 1994 ident: ref12 article-title: Adaptive time-frequency decompositions publication-title: Opt Eng doi: 10.1117/12.173207 – year: 2014 ident: ref1 publication-title: Hyperspectral Remote Sensing Scenes – ident: ref4 doi: 10.1109/TSP.2006.881199 – volume: 14 start-page: 2487 year: 2013 ident: ref18 article-title: Greedy feature selection for subspace clustering publication-title: J Mach Learn Res – ident: ref32 doi: 10.1162/neco.2007.19.10.2756 – volume: 40 start-page: 1 year: 2008 ident: ref43 article-title: Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit publication-title: CS Technion – volume: 16 start-page: 285 year: 2015 ident: ref33 article-title: An asynchronous parallel stochastic coordinate descent algorithm publication-title: J Mach Learn Res – year: 2015 ident: ref38 publication-title: Introduction to Linear Regression Analysis – ident: ref15 doi: 10.1023/B:MACH.0000033113.59016.96 – start-page: 113 year: 2010 ident: ref10 article-title: On the impact of kernel approximation on learning accuracy publication-title: Proc TAISTATS – year: 0 ident: ref3 publication-title: Rankmap APIs – start-page: 689 year: 2000 ident: ref48 article-title: Generalized belief propagation publication-title: Proc NIPS – start-page: 567 year: 2013 ident: ref24 article-title: Revisiting the Nyström method for improved large-scale machine learning publication-title: Proc ICML – start-page: 1657 year: 2015 ident: ref44 article-title: Less is more: Nyström computational regularization publication-title: Proc NIPS – start-page: 340 year: 2010 ident: ref34 article-title: GraphLab: A new parallel framework for machine learning publication-title: Proc UAI – year: 0 ident: ref2 publication-title: The Light Field Archive – ident: ref46 doi: 10.1016/0024-3795(72)90013-4 – ident: ref47 doi: 10.1109/JPROC.2010.2044470 – year: 2015 ident: ref27 publication-title: Eigen C++ template library for linear algebra – start-page: 2 year: 2012 ident: ref49 article-title: Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing publication-title: Proc NSDI – start-page: 10 year: 2010 ident: ref50 article-title: Spark: Cluster computing with working sets publication-title: Proc HotCloud |
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| SubjectTerms | Algorithms Computational modeling Computer networks Data recovery Data structures Datasets Dense and big data Distributed databases Distributed processing Iterative algorithms iterative machine learning (ML) Iterative methods large-scale distributed computing Learning algorithms low rank approximation Machine learning Matrix decomposition Memory Partitioning algorithms Signal processing algorithms Sparse matrices sparse matrix factorization Subspaces union of subspaces |
| Title | RankMap: A Framework for Distributed Learning From Dense Data Sets |
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