PPT-Using In-Memory, Data-Parallel Computing for Operational In

Copyright 2014 by ScaleOut Software Inc Portland Big Data Users Group October 23 2014 Bill Bain CEO wbainscaleoutsoftwarecom What Is Operational Intelligence Example Tracking Cable Viewers. 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 . Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . 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 . 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. 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?. 2. Chapter 9 Objectives. Learn the properties that often distinguish RISC from CISC architectures.. Understand how multiprocessor architectures are classified.. Appreciate the factors that create complexity in multiprocessor systems.. 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 . 2. Chapter 9 Objectives. Learn the properties that often distinguish RISC from CISC architectures.. Understand how multiprocessor architectures are classified.. Appreciate the factors that create complexity in multiprocessor systems.. 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 ( Tennessee State University. 2017. 年. 6. 月. at. 法政大学. 1. Lectures on Parallel and Distributed Computing . 2. Lecture . 1: Introduction to parallel . computing . Lecture 2: Parallel . computational models. 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.