PDF-Parallel Data Processing with MapReduce A Survey Kyong

com yoonjoonleekaistackr Hyunsik Choi Yon Dohn Chung Department of Computer Science and Engineering Korea University hyunsikchoikoreaackr ydchungkoreaackr Bongki Moon Department of Computer Science University of Arizona bkmooncsarizonaedu ABSTRACT A. 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 . “MapReduce and parallel DBMSs: friends or foes?” Stonebraker, Daniel Abadi, David J Dewitt et al.. MAP REDUCE AND PARALLEL DBMS ARE COMPLEMENTARY. In 2010, . MapReduce. (MR) has been hailed as a . 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 : . 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, . 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.. MapReduce. on Multicore Clusters. UMBC, Maryland. Judy Qiu. http://salsahpc.indiana.edu . School . of Informatics and Computing. Pervasive Technology Institute. Indiana University. Important Trends. Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. 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. Mohammadhossein . Behgam. Agenda. Need for parallelism. Challenges. Image processing algorithms. Data handling & Load Balancing. Communication cost & performance. What is the problem?. Image Processing applications can be very computationally demanding due to:. ”. 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. Jeffrey Dean & . Sanjay . Ghemawat. Appeared in:. OSDI '04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, December, 2004. . Presented by: . Hemanth. . Makkapati. ," said Rich Miller, who edits Data Center Knowledge, a publication that follows the data center industry. "It's buildings filled with servers and generators.". “…. . you'd be hard-pressed to go three miles here without passing some type of data center. "They're all over the place.".