PPT-1 The Parallel Computing Landscape:

A View from Berkeley Dave Patterson Parallel Computing Laboratory UC Berkeley July 2008 Outline What Caused the Revolution Is it an Interesting Important Research Problem or Just Doing Industrys Dirty Work. 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 Shapiro Didway designed the overall campus and playground of the Redding School of the Arts, a K-8 arts charter school located in Redding, California. This LEED Platinum project incorporates native plants, reclaimed rainwater runoff, and traffic calming elements. Extensive natural shade improvements mitigate the intense weather of the area. Extensive efforts were made to preserve and protect numerous Blue Oaks on site and incorporate them into the overall site plan. 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-. Copyright © 2014 by ScaleOut Software, Inc.. Portland Big Data Users Group. October 23, 2014. Bill . Bain, CEO . (. wbain@scaleoutsoftware.com). What Is Operational Intelligence?. Example: Tracking Cable Viewers. 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 . Applications and. Types of Parallelism. Henry Neeman, Director. OU Supercomputing Center for Education & Research. University of Oklahoma Information Technology. Oklahoma Supercomputing Symposium, Tue Oct . with . OpenMP. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering . Lab. Wisconsin Applied Computing Center. Department of Mechanical . Engineering. Department of . Electrical and Computer Engineering. 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. Our . life seems to be impossible without art. It really occupies an important part in our daily . life. .. . Also it makes our life brighter. , richer and more intellectual. So, art units different people, influences the development of personality, makes our . 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 ( 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.