PPT-Graph-Based Parallel Computing
Author : yoshiko-marsland | Published Date : 2019-03-16
William Cohen 1 Computing paradigms Streamandsort Iterative streaming ML eg SGD with minibatch vectorization and GPUs Mapreduce streamandsort parallelism plus
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Graph-Based Parallel Computing: Transcript
William Cohen 1 Computing paradigms Streamandsort Iterative streaming ML eg SGD with minibatch vectorization and GPUs Mapreduce streamandsort parallelism plus dataflowlanguage abstractions. Unlike sequential algorithms parallel algorithms cannot be analyzed very well in isolation One of our primary measures of goodness of a parallel system will be its scalability Scalability is the ability of a parallel system to take advantage of incr 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 Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. What is the fork-join concept?. What is the fork-join pattern?. Programming Model Support for Fork-Join. Recursive Implementation of Map. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . 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. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . Parallel Computing. CIS . 410/. 510. Department of Computer and Information Science. Outline. Quick review of hardware architectures. Running on supercomputers. Message Passing. MPI. 2. Introduction to Parallel Computing, University of Oregon, IPCC. 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. William Cohen. 1. Announcements. Next Tuesday 12/8:. Presentations for 10-805 projects.. 15 minutes per project.. Final written reports due Tues 12/. 15. For exam:. S. pectral clustering will not be cov. Dr Susan Cartwright. Dept of Physics and Astronomy. University of Sheffield. Parallel Universes. Are you unique?. Could there be another “you” differing only in what you had for breakfast this morning?. 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. 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.
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