PPT-Graph-Based Parallel Computing

William Cohen 1 Announcements Next Tuesday 128 Presentations for 10805 projects 15 minutes per project Final written reports due Tues 12 15 For exam S pectral clustering will not be cov. Performance Theory - 1. Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. Performance scalability. Analytical performance measures. Amdahl. ’. s. law and Gustafson-. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . George Caragea, and Uzi Vishkin. University of Maryland. 1. Speaker. James Edwards. It has proven to be quite . difficult. to obtain significant performance improvements using current parallel computing platforms.. Andrew Lumsdaine. Indiana University. lums@osl.iu.edu. My Goal in Life. Performance with elegance. Introduction. Overview of our high-performance, industrial strength, graph library. Comprehensive features. Going Beyond Serial MATLAB Applications. MATLAB . Desktop (Client). Worker. Worker. Worker. Worker. Worker. Worker. Programming Parallel Applications (CPU). Built-in support. with t. oolboxes. Ease of Use. : . Structure . and . Algorithms*. Presented By:. Saleh. A. . Almugrin. salmugri@kent.edu. * Based . and influenced by . many works of . Hans L. . Bodlaender. , . Institute of Information and Computing Sciences, Utrecht . A View from Berkeley. Dave Patterson. Parallel Computing Laboratory. U.C. Berkeley. July, 2008. Outline. What Caused the Revolution?. Is it an Interesting, Important Research Problem or Just Doing Industry’s Dirty Work?. How to Use Parallel Computing Toolbox™ and MATLAB® Distributed Computing Server™ on Discovery Cluster, . An EECE5640: High Performance Computing lecture. Benjamin Drozdenko. MathWorks TA & Graduate Research Assistant . 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. William Cohen. 1. Computing paradigms. Stream-and-sort. Iterative streaming ML (. eg. SGD. ). with . minibatch. vectorization and GPUs. Map-reduce (stream-and-sort parallelism). plus dataflow-language abstractions. Early Adopter: ASU - Intel Collaboration in Parallel and Distributed Computing Yinong Chen , Eric Kostelich , Yann -Hang Lee, Alex Mahalov , Gil Speyer, and Violet R. Syrotiuk 1 st NSF /TCPP Workshop on Parallel and Distributed Computing Education ( CS 6260. Professor: Elise De . Doncker. By: . Lina. Hussein. 1. Topics Covered :. Introduction. What is cluster computing?. Classification of Cluster Computing. Technologies:. Beowulf cluster. Construction of Beowulf Cluster.