Managed communication and consistency for fast data-parallel iterative analytics
暂无分享,去创建一个
Eric P. Xing | Henggang Cui | Gregory R. Ganger | Phillip B. Gibbons | Garth A. Gibson | Wei Dai | Qirong Ho | Jinliang Wei | Aurick Qiao | E. Xing | Qirong Ho | Henggang Cui | G. Ganger | Wei Dai | Jinliang Wei | Aurick Qiao
[1] John W. Young,et al. A first order approximation to the optimum checkpoint interval , 1974, CACM.
[2] Leslie G. Valiant,et al. A bridging model for parallel computation , 1990, CACM.
[3] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[4] W. Gilks. Markov Chain Monte Carlo , 2005 .
[5] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[6] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[7] David Madigan,et al. Large-Scale Bayesian Logistic Regression for Text Categorization , 2007, Technometrics.
[8] Dennis M. Wilkinson,et al. Large-Scale Parallel Collaborative Filtering for the Netflix Prize , 2008, AAIM.
[9] Sanjay Ghemawat,et al. MapReduce: simplified data processing on large clusters , 2008, CACM.
[10] John Langford,et al. Slow Learners are Fast , 2009, NIPS.
[11] Jieping Ye,et al. Large-scale sparse logistic regression , 2009, KDD.
[12] Andrew McCallum,et al. Efficient methods for topic model inference on streaming document collections , 2009, KDD.
[13] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[14] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[15] Aart J. C. Bik,et al. Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.
[16] Alexander J. Smola,et al. An architecture for parallel topic models , 2010, Proc. VLDB Endow..
[17] Shou-De Lin,et al. Feature Engineering and Classifier Ensemble for KDD Cup 2010 , 2010, KDD 2010.
[18] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[19] Joseph E. Gonzalez,et al. GraphLab: A New Parallel Framework for Machine Learning , 2010 .
[20] Jinyang Li,et al. Piccolo: Building Fast, Distributed Programs with Partitioned Tables , 2010, OSDI.
[21] Albert G. Greenberg,et al. Reining in the Outliers in Map-Reduce Clusters using Mantri , 2010, OSDI.
[22] Scott Shenker,et al. Spark: Cluster Computing with Working Sets , 2010, HotCloud.
[23] Jinyang Li,et al. Building fast, distributed programs with partitioned tables , 2010 .
[24] Joseph K. Bradley,et al. Parallel Coordinate Descent for L1-Regularized Loss Minimization , 2011, ICML.
[25] Stephen J. Wright,et al. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.
[26] Peter J. Haas,et al. Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.
[27] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[28] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[29] Carlos Guestrin,et al. Distributed GraphLab : A Framework for Machine Learning and Data Mining in the Cloud , 2012 .
[30] Alexander J. Smola,et al. Scalable inference in latent variable models , 2012, WSDM '12.
[31] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[32] Joseph Gonzalez,et al. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs , 2012, OSDI.
[33] Garth A. Gibson,et al. PRObE: A Thousand-Node Experimental Cluster for Computer Systems Research , 2013, login Usenix Mag..
[34] Seunghak Lee,et al. More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server , 2013, NIPS.
[35] Aaron Q. Li,et al. Parameter Server for Distributed Machine Learning , 2013 .
[36] Scott Shenker,et al. Usenix Association 10th Usenix Symposium on Networked Systems Design and Implementation (nsdi '13) 185 Effective Straggler Mitigation: Attack of the Clones , 2022 .
[37] Martin Wattenberg,et al. Ad click prediction: a view from the trenches , 2013, KDD.
[38] M. Abadi,et al. Naiad: a timely dataflow system , 2013, SOSP.
[39] Seunghak Lee,et al. Solving the Straggler Problem with Bounded Staleness , 2013, HotOS.
[40] Alexander J. Smola,et al. Scaling Distributed Machine Learning with the Parameter Server , 2014, OSDI.
[41] Eric P. Xing,et al. Exploiting iterative-ness for parallel ML computations , 2014, SoCC.
[42] Matthew J. Streeter,et al. Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning , 2014, NIPS.
[43] Seunghak Lee,et al. Exploiting Bounded Staleness to Speed Up Big Data Analytics , 2014, USENIX Annual Technical Conference.
[44] Seunghak Lee,et al. On Model Parallelization and Scheduling Strategies for Distributed Machine Learning , 2014, NIPS.
[45] Patrick Seemann,et al. Matrix Factorization Techniques for Recommender Systems , 2014 .
[46] Trishul M. Chilimbi,et al. Project Adam: Building an Efficient and Scalable Deep Learning Training System , 2014, OSDI.
[47] Eric P. Xing,et al. High-Performance Distributed ML at Scale through Parameter Server Consistency Models , 2014, AAAI.