PPT-Map-Reduce Graph Processing

Adapted from UMD Jimmy Lins slides which is licensed under a Creative Commons AttributionNoncommercialShare Alike 30 United States See http creativecommonsorg licensesby nc . R. e. d. u. c. e. . Simplified Data Processing on Large Clusters. (Without the Agonizing Pain). Presented by Aaron Nathan. The Problem. Massive amounts of data. >100TB (the internet). Needs simple processing. Nima Sarshar, Ph.D.. INTUIT . Inc. ,. Nima_sarshar@intuit.com . Intuit, . Graphs and Me. Me: . Large-scale graph data processing, complex networks analysis, graph algorithms … . Intuit: . QuickBooks, TurboTax, . Recap: data-intensive cloud computing. Just database management on the cloud. But scaling it to thousands of nodes. Handling partial failures gracefully. Sacrificing strong ACID. Graph processing frameworks (. Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. dimensional data . sets into . simplicial complexes . with far . fewer points . which can . capture topological . and geometric information at . a specified resolution. .. v. 2. e. 2. e. 1. e. 3. v. 1. Hao Wei. 1. , . Jeffrey Xu Yu. 1. , Can L. u. 1. , . Xuemin. Lin. 2. . 1 . The . Chinese University of Hong Kong, Hong Kong. 2 . The . University of New South Wales. , . Sydney, Australia. Graph in Big Data . Sang-Woo Jun,. . Andy Wright, . Sizhuo. Zhang, . Shuotao. Xu and . Arvind. MIT CSAIL. 1. Funded by:. Large Graphs are Found . Everywhere in Nature. 2. Human neural network. 1) . Connectomics. graph of the brain - Source: “. William Cohen. 1. Announcements. Next Tuesday 12/8:. Presentations for 10-805 projects.. 15 minutes per. project.. Final written reports due Tues 12/15. 2. Graph-Based Parallel Computing. William Cohen. Data-Intensive Distributed Computing Part 4: Analyzing Graphs (2/2) 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 . with Distributed Immutable View. Rong Chen. +. , . Xin. Ding. +. , . Peng. Wang. +. , Haibo Chen. +. , . Binyu . Zang. +. and Haibing Guan. *. Institute of Parallel and Distributed Systems. +. Stijn Eyerman, Wim Heirman, Ibrahim Hur, Joshua B. Fryman. 2. DARPA HIVE project. “To build a graph analytics processor that can process streaming graphs 1000X faster and at much lower power than current processing technology”. Madhu M Nayak Assistant Professor, Department of C SE, GSSSIETW, Mysuru Pradeep.S Assistant Professor, Department of C SE, GEC, Kushal Nagar Abstract - Due to the increasing popularity of cheap The Problem . Large Graphs are often part of computations required in modern systems (Social networks and Web graphs etc.). There are many . graph . computing problems like shortest path, clustering, page rank, minimum cut, connected components . Source. MapReduce. : Simplified Data Processing in Large Clusters. . Jefferey. Dean and Sanjay . Ghemawat. . OSDI 2004. Example Scenario. 3. Genome data from roughly . one million users. 125 MB of data per user.