PPT-Speedup Graph Processing by Graph Ordering

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 . . Siddharth. . Choudhary. What is Bundle Adjustment ?. Refines a visual reconstruction to produce jointly optimal 3D structure and viewing parameters. ‘bundle’ . refers to the bundle of light rays leaving each 3D feature and converging on each camera center. . Aapo. Kyrola. Danny. Bickson. A Framework for Machine Learning and Data Mining in the Cloud . Joseph. Gonzalez. Carlos. Guestrin. Joe. Hellerstein. Big Data is Everywhere. 72 Hours . a Minute. YouTube. 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 (. Isabelle Stanton, UC Berkeley. Gabriel . Kliot. , Microsoft Research XCG. Modern graph datasets are huge. The web graph had over a trillion links in 2011. Now?. . facebook. has “more than 901 million users with average degree 130”. Adapted from UMD Jimmy Lin’s slides, which . is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United . States. See . http://. creativecommons.org. /licenses/by-. nc. -. 3. . Sparse Direct Method: . Combinatorics. Xiaoye . Sherry Li. Lawrence Berkeley National . Laboratory. , . USA. xsli@. lbl.gov. crd-legacy.lbl.gov. /~. xiaoye. /G2S3/. 4. th. Gene . Golub. SIAM Summer . Section . 10.3. Representing Graphs: . Adjacency Lists. Definition. : An . adjacency list . can be used to represent a graph with no multiple edges by specifying the vertices that are adjacent to each vertex of the graph.. Bickson. A Framework for Machine Learning and Data Mining in the Cloud . Joseph. Gonzalez. Carlos. Guestrin. Joe. Hellerstein. Big Data is Everywhere. 72 Hours . a Minute. YouTube. 28 . Million . Wikipedia Pages. 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 Structures and Algorithms. CSE 373 19 SP - Kasey Champion. 1. Administrivia. We clarified Exercise 5 Problem [] to explicitly mention “worst-case”. CSE 373 SP 18 - Kasey Champion. 2. Administrivia. . 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”. 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 . Announcements. Talk on technical interviews . today!. Gugenheim. 220 at 1:10 PM.. Para Exercise feedback soon.. P2 Feedback (hopefully) Saturday.. Announcements. Please fill out course evaluations..