PPT-MapReduce : Simplified Data Processing on Large Clusters

Jeffrey Dean amp Sanjay Ghemawat Appeared in OSDI 04 Sixth Symposium on Operating System Design and Implementation San Francisco CA December 2004 Presented by Hemanth Makkapati. 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. 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. Computations. K-means. Performance of K-Means. Smith Waterman is a non iterative case and of course runs fine. Matrix Multiplication . 64 cores. Square blocks Twister. Row/Col . decomp. Twister. Giorgio Busoni. 1. Based. on. : . arXiv:1409.2893 (and 1307.2253, 1402.1275, . 1405.3101. , 1402.2285) . Oxford, 27 September 2014. Outline. Problems with EFT approach in Mono-X searches. From EFT to Simplified models. by Mahedi Hasan. 1. Table of Contents. Introducing Cluster Concept. About Cluster Computing. Concept of whole computers and it’s benefits. Architecture and Clustering Methods. Different clusters catagorizations. By . Yufei. Tao, . Wenqing. . Lin, . Xiaokui. . Xia. Edited by Tuval Rotem. First topic:. What is MapReduce. But first…. Regular day at work. You’re doing your regular work while suddenly your boss, . 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 . 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.. 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. Computations. K-means. Performance of K-Means. Smith Waterman is a non iterative case and of course runs fine. Matrix Multiplication . 64 cores. Square blocks Twister. Row/Col . decomp. Twister. Sixth International Workshop on Cloud Data . Management. CloudDB. 2014. Chicago March 31 2014. Geoffrey . Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. ”. 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. Kobe pipelines simplified. 2. Riku-Pekka Silvola. Configuring Kobe pipeline. Configured with . .. gitlab-ci.yml. in your project. Documentation available on . GitLab. 9/30/2019. Kobe pipelines simplified. Used slides from RAD Lab at UC Berkeley about the cloud ( . http://abovetheclouds.cs.berkeley.edu/. ) and slides from Jimmy Lin. ’. s slides (http://www.umiacs.umd.edu/~jimmylin/cloud-2010-Spring/index.html) (licensed under Creation Commons Attribution 3.0 License).