PPT-Efficient Graph Processing

with Distributed Immutable View Rong Chen Xin Ding Peng Wang Haibo Chen Binyu Zang and Haibing Guan Institute of Parallel and Distributed Systems . Multigrid. Solver for. (Evolving) Poisson Systems on Meshes. Misha. . Kazhdan. Johns Hopkins University. Motivation. Image Stitching. Compute image gradients. Set seam-crossing gradients to zero. Fit image to the new gradient field . Embedded CPU-GPU Architectures. Xuntao Cheng. , Bingsheng He, Chiew Tong Lau. Nanyang Technological University, Singapore. 1. Outline. Motivations. System Design. Evaluations. Conclusion. 2. Query Processing in the Era of IoT. 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 (. 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. -. Graph . Processing . . with Distributed Immutable View. Rong Chen. +. , . Xin. Ding. +. , . Peng. Wang. +. , Haibo Chen. +. , . Binyu . Zang. +. and Haibing Guan. *. Institute of Parallel and Distributed Systems. 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.. 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 . 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.. 1. Learning Objectives:. Know how to use graphs as models and how to determine efficient paths.. Modeling with graphs. Euler circuits. Degrees of vertices and Euler’s Theorem. Chapter . 7 Graph Theory. 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. Applied Minimal Processing Strategies. John . Nemmers. , Descriptive and Technical Services Archivist, UF. Annie Benefiel, Processing Archivist, UF. May 7, 2014. Society of Florida Archivists . Annual Meeting, Orlando FL. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. 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 .