PPT-MapReduce

Author : alexa-scheidler | Published Date : 2015-09-21

With a heavy debt to Google Map Reduce OSDI 2004 slides codegooglecom You are an engineer at Harebrainedschemecom Your boss comes to your office and says Were

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MapReduce: Transcript

With a heavy debt to Google Map Reduce OSDI 2004 slides codegooglecom You are an engineer at Harebrainedschemecom Your boss comes to your office and says Were going to be hognasty rich. Tsung. -Wei Huang. and Martin D. F. Wong. Department of Electrical and Computer Engineering (ECE). University of Illinois at Urbana-Champaign (UIUC), IL, USA. 2015 ACM International Symposium on Physical Design (ISPD). Cloud, Part II: Search => Cluster Apps => Scalable Machine Learning. David E. Culler. . CS162 – Operating Systems and Systems Programming. Lecture 40. December 3, . 2014. Proj. : CP 2 . today. Runhui Li, . Patrick P. C. Lee. , . Yuchong. Hu. The Chinese University of Hong Kong. DSN’14. Motivation. Clustered storage systems. . are widely . deployed . (e.g. ., GFS, HDFS, Azure). Stripe data across multiple nodes. IUPUI Computer Science. February 11 2011. Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . Director, Digital Science Center, Pervasive Technology Institute. , Collective Communication, and Services. Oral Exam, . Bingjing. Zhang. Outline. MapReduce. MapReduce. Frameworks. Iterative . MapReduce. Frameworks. Frameworks Based on . MapReduce. and Alternatives. Simplified Data Processing on Large . Clusters. by Jeffrey Dean and Sanjay . Ghemawa. Presented by Jon Logan. Outline. Problem Statement / Motivation. An Example Program. MapReduce. . vs. Hadoop. GFS / HDFS. Paradigms Activity at Data Intensive Workshop. . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu. Parallel Computing. MapReduce. Examples. Parallel Efficiency. Assignment. Parallel Computing. Parallel efficiency with . p. processors. Traditional parallel computing:. focus on compute intensive tasks. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . . Shantenu Jha represented by . Geoffrey Fox. gcf@indiana.edu. . . http://www.infomall.org. . http://www.futuregrid.org. . . http://salsahpc.indiana.edu/. . Director, Digital Science Center, Pervasive Technology Institute. Presented By. Shefali. . Gundecha. Srinivas . Narne. Yash. Kulkarni. Papers to be discussed…. Y. Shan, B. Wang, J. Yan, Y. Wang, N. Xu, and H. Yang, . " FPMR: MapReduce Framework on FPGA: A Case Study of . ”. Cathy O’Neil & Rachel . Schutt. , 2013. R & Hadoop. Compute squares. 2. R. # create a list of 10 integers. ints. <- 1:10. # equivalent to . ints. <- c(1,2,3,4,5,6,7,8,9,10). # compute the squares. Jimmy Lin. The iSchool. University of Maryland. Monday, March 30, 2009. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States. See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details. MapReduce. Architecture. MapReduce. Internals. MapReduce. Examples. JobTracker. Interface. MapReduce. : A Real World Analogy. Coins Deposit. ?. MapReduce. : A Real World Analogy. Coins Deposit. Coins Counting Machine.

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