PPT-Scaling Distributed Machine Learning with the Parameter Ser

Author : stefany-barnette | Published Date : 2017-12-29

By M Li D Anderson J Park A Smola A Ahmed V Josifovski J Long E Shekita B Su EECS 582 W16 1 Outline Motivation Parameter Server architecture Why is it special

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Scaling Distributed Machine Learning with the Parameter Ser: Transcript

By M Li D Anderson J Park A Smola A Ahmed V Josifovski J Long E Shekita B Su EECS 582 W16 1 Outline Motivation Parameter Server architecture Why is it special. apollovalves com Andersen Jun Woo Park Alexander J Smola Amr Ahmed Vanja Josifovski James Long Eugene J Shekita BorYiing Su Carnegie Mellon University Baidu Google muli dga junwoop cscmuedu alexsmolaorg amra vanjaj jamlong shekita boryiingsu googlecom Abstract Presented by Paul. Problem. The cloud offers. Near infinite computing capabilities. Near infinite storage. On-demand access to new VMs. Pay-as-you-go model. But how do you build an application to automatically take advantage of those resources?. . Networks. Distributed. . P. arameter. . Networks. Distributed. . Parameter. . Networks. 1.. The . electric. and . magnetic. . power. . distribute. . homogeneously. . along. . the. . wire. National Consultation on. Community Action for Health. October 28 - 29, 2014. Day One Proceedings . Context setting inaugural. Five thematic sessions:. State experiences of Community Based Planning and Monitoring – Bihar, Maharashtra and Tamil Nadu . Pochung Chen . 陳柏中. National . Tsing. . Hua. University, Taiwan. 10/14/2013, IWCSE, NTU. Acknowledgement. Collaborators. Zhi. -Long . Xue. (NTHU). Ian . P. . McCulloch (UQ, Australia). Ming-Chiang Chung (NCHU). COS 518: Advanced Computer Systems. Lecture . 13. Daniel Suo. Outline. 2. What is machine learning?. Why is machine learning hard in parallel / distributed systems?. A brief history of what people have done. Design-Technology . Co-optimization at the Rescue. S. m. Y. . . Sherazi. & J. Ryckaert. . with . contributions from all . insite. . team. . ISPD . 2016. Invited Talk. Design-Technology co-optimization as process technology pathfinder . p. 41 & 42 in math book. Multiplying or dividing two related quantities by the same number is called . scaling. . Sometimes you may need to scale back and then scale forward to find an equivalent ratio.. Corey . Pentasuglia. Masters Project. 5/11/2016. Examiners. Dr. Scott . Spetka. Dr. . Bruno . Andriamanalimanana. Dr. Roger . Cavallo. Masters Project Objectives. Research DML (Distributed Machine Learning). Big Learning?. A Distributed Systems Perspective. . Phillip B. Gibbons. Carnegie Mellon University. ICDCS’16 Keynote Talk, June 28, 2016. What’s So Special about…Big Data?. Keynote #2: Prof. Masaru . Big Learning?. A Distributed Systems Perspective. . Phillip B. Gibbons. Carnegie Mellon University. ICDCS’16 Keynote Talk, June 28, 2016. What’s So Special about…Big Data?. Keynote #2: Prof. Masaru . Sebastian . Schelter. , . Venu. . Satuluri. , Reza . Zadeh. Distributed Machine Learning and Matrix Computations workshop in conjunction with NIPS 2014. Latent Factor Models. Given . M. sparse. n . x . Abid M. Malik. Meifeng. Lin (PI). Collaborators: Amir . Farbin. (UT) , Jean . Roch. ( CERN). Computer Science and Mathematic Department. Brookhaven National Laboratory (BNL). Distributed ML for HEP.

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