PPT-Parallel and Distributed Computing

Early a nd Often Steven Bogaerts speaker Kyle Burke Brian Shelburne Department of Mathematics amp Computer Science Wittenberg University Springfield OH Melissa Smith Department of Electrical . Uni processor computing can be called centralized computing brPage 3br mainframe computer workstation network host network link terminal centralized computing distributed computing A distributed system is a collection of independent computers interc 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 . Improving Computer Performance. What performance translates into:. Time taken to do computation. Improving performance . → reducing time taken. What key benefits improving performance can bring:. Can solve “now-computationally-attainable” problems in . :. Teaching and Training for . the Parallel Future. Dick Brown. St. Olaf College. SPLASH Educators’ and Trainers Symposium. October 24, 2011. Overview. Review of the need for parallelism. Strategies we need. Set up a large number of machines all identically configured. Connect them to a high speed LAN. And to the Internet. Accept arbitrary jobs from remote users. Run each job on one or more nodes. Entire facility probably running mix of single machine and distributed jobs, simultaneously. 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?. Recall: Microprocessors are classified by how memory is organized. Tightly-coupled multiprocessor systems use the same memory. They are also referred to as . shared memory multiprocessors. .. The processors do not necessarily have to share the same block of physical memory: . 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 ( 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. Dr. . Barsha. . Mitra. CSIS . Dept. , BITS Pilani, Hyderabad Campus. Introduction. Course ID: SS ZG526, Title: Distributed Computing. allows for flexibly sharing resources (e.g., files and multimedia documents) stored across network-wide computers. Introduction. Papadakis Harris. Dept. of Engineering Informatics. ΤΕΙ . of Crete. Distributed Systems. - . Contents. Definition. Parallel. . Processing. Distributed . Computin. Clusters. Examples.