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As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands.
The hadoop distributed file system: Architecture and design
Dhruba Borthakur · 2007
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Vowpal wabbit online learning project, 2007
J Langford, L Li, and A Strehl · 2007
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Plda: Parallel latent dirichlet allocation for large-scale applications
Yi Wang, Hongjie Bai, Matt Stanton, Wen-Yen Chen, and Edward Y. Chang · 2009
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Graphlab: A new parallel framework for machine learning
Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, and Joseph M. Hellerstein · 2010
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Spark: Cluster computing with working sets
Matei Zaharia, N. M. Mosharaf Chowdhury, Michael Franklin, Scott Shenker, and Ion Stoica · 2010
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Parallel coordinate descent for l1-regularized loss minimization
Joseph K. Bradley, Aapo Kyrola, Danny Bickson, and Carlos Guestrin · 2011
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Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
Feng Niu, Benjamin Recht, Christopher Ré, and Stephen J Wright · 2011
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Scalable inference in latent variable models
Amr Ahmed, Moahmed Aly, Joseph Gonzalez, Shravan Narayanamurthy, and Alexander J. Smola · 2012
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Large scale distributed deep networks
J Dean, G Corrado, R Monga, K Chen, M Devin, Q Le, M Mao, M Ranzato, A Senior, P Tucker, K Yang, and A Ng · 2012
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Scalable coordinate descent approaches to parallel matrix factorization for recommender systems
Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, and Inderjit S Dhillon · 2012
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Solving the straggler problem with bounded staleness
James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Gregory R. Ganger, Garth Gibson, Kimberly Keeton, and Eric Xing · 2013
Cited alongside, same era.
More effective distributed ml via a stale synchronous parallel parameter server
Parameter server for distributed machine learning
Mu Li, Li Zhou Zichao Yang, Aaron Li Fei Xia, David G. Andersen, and Alexander Smola · 2013
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Parameter server for distributed machine learning, big learning workshop
Mu Li, Li Zhou, Zichao Yang, Aaron Li, Fei Xia, Dave Andersen, and Alex Smola · 2013
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Project adam: Building an efficient and scalable deep learning training system
Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman · 2014
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Exploiting bounded staleness to speed up big data analytics
Henggang Cui, James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Abhimanu Kumar, Jinliang Wei, Wei Dai, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, and Eric P. Xing · 2014
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Primitives for dynamic big model parallelism
Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A Gibson, and Eric P Xing · 2014
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Scaling distributed machine learning with the parameter server
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Q. Ho, J. Cipar, H. Cui, J.-K. Kim, S. Lee, P. B. Gibbons, G. Gibson, G. R. Ganger, and E. P. Xing · 2013
Cited alongside, same era.
Fugue: Slow-worker-agnostic distributed learning for big models on big data
Abhimanu Kumar, Alex Beutel, Qirong Ho, and Eric P. Xing
Cited in the paper.
Mu Li, David G. Andersen, Jun Woo Park, Alexander J. Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J. Shekita, and Bor-Yiing Su · 2014
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