PPT-Parallel computing with MATLAB

Going Beyond Serial MATLAB Applications MATLAB Desktop Client Worker Worker Worker Worker Worker Worker Programming Parallel Applications CPU Builtin support with t oolboxes Ease of Use. P. arallel . P. rocessing. G. uy . T. el-. Z. ur. L. ecture 8. The Course Roadmap. Introduction. Message Passing. HTC. HPC. Shared Memory. Condor. Cloud Computing. MPI. OpenMP. T. oday. Today. Next week. 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-. I. ntegrated . R. esearch . C. omputing. MATLAB. http://. www.circ.rochester.edu. /wiki/. index.php. /. MatlabWorkshop. Outline. Part I – Interacting with . Matlab. Running . Matlab. interactively. 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. Dr. Guy Tel-. Zur. Lecture 10. Agenda. Administration. Final presentations. Demos. Theory. Next week plan. Home assignment #4 (last). Final Projects. Next Sunday: Groups 1-1. 6. will present. Next Monday: Groups 1. 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 . Luke Winslow. OWI a-la-carte. Overview. What is Parallel Computing. Example Uses. Intro to Condor. Demo. Parallel Computing. Cluster. Why would I need this?. Dealing with Large Datasets. Lake Shape Characteristics. Wendy Thomas. Associate Professor of Bioengineering . University of Washington. 1. My Teaching Experience. . Bioen. 201 (2008 – 2010) sophomore core: intro to mathematical programming (5 weeks) and circuits (5 weeks). 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 . 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 ( Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. 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. Mark Reed . Lani Clough. Objectives. Intermediate. level MATLAB course for people . already using. . MATLAB.. Help participants . go past the basics . and improve the performance their MATLAB code. .