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 1215 2 GraphBased Parallel Computing William Cohen. 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 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. 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. 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. Applications and. Types of Parallelism. Henry Neeman, Director. OU Supercomputing Center for Education & Research. University of Oklahoma Information Technology. Oklahoma Supercomputing Symposium, Tue Oct . : . 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 . 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. 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. 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. 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.