PPT-Investigation of Data Locality in MapReduce

Zhenhua Guo Geoffrey Fox Mo Zhou Outline Introduction Analysis of Data Locality Optimality of Data Locality Experiments Conclusions MapReduce Execution Overview 3 Google File System Read input data. S. A. L. S. A. . HPC Group . http://. salsahpc.indiana.edu. School of Informatics and Computing. Indiana University. Judy . Qiu. . Thilina. . Gunarathne. . CAREER Award. Outline. Iterative . Optimization. Techniques. . Presented by . Preethi Rajaram. CSS 548 Introduction to Compilers . Professor Carol Zander. Fall 2012 . Why?. Processor Speed . -. increasing at a faster rate than the memory speed. and . Hadoop. Debapriyo Majumdar. Data Mining – Fall 2014. Indian Statistical Institute Kolkata. November 10, 2014. Let’s keep the intro short. Modern data mining: process immense amount of data quickly. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Data Locality and Fairness. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Data locality. Zhenhua . Guo. PhD Thesis Proposal. Outline. Introduction and Motivation. Literature Survey. Research Issues and Our Approaches. Contributions. 2. Traditional HPC Architecture vs. the Architecture of Data Parallel Systems. Parallel Computing. MapReduce. Examples. Parallel Efficiency. Assignment. Parallel Computing. Parallel efficiency with . p. processors. Traditional parallel computing:. focus on compute intensive tasks. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Analysis of Data Locality. Optimality of Data Locality. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Practical methodology for Healthy Economies . You can do it! . No really, you can! . HERE’S . HOW. MAKE A PLAN - STRATEGIZE. MAKE A BUDGET. ASSESS YOUR CURRENT SITUATION. IDENTIFY WHAT IS WORKING. Yasin N. Silva and Jason Reed. Arizona State University. 1. This work is licensed under a Creative Commons Attribution-. NonCommercial. -. ShareAlike. 4.0 International License. See http://creativecommons.org/licenses/by-nc-sa/4.0/ for details.. Arran 20. th. June 2016. Agenda. 6.30 – 6.45 . Introduction . and Background to Locality Partnerships .  . 6.45 – 7.15 . Data . – Information we have on Localities.  . 7.15 – 7.30 . Madhu. . Sudan. Harvard. April 9, 2016. Skoltech: Locality in Coding Theory. 1. Error-Correcting Codes. (Linear) Code . .. : Finite field with . elements.. . block length. . message length. : Rate of . Madhu Sudan. Harvard. April 9, 2016. Skoltech: Locality in Coding Theory. 1. Outline of this Part. Three ideas in Testing:. Tensor Products. Composition/Recursion. Symmetry (esp. affine-invariance. ). 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.