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. Avg Access Time 2 Tokens Number of Controllers Average Access Time clock cyles brPage 16br Number of Tokens vs Avg Access Time 9 Controllers Number of Tokens Average Access Time clock cycles brPage 17br brPage 18br 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 Hardware Parallelism. Computing: execute instructions that operate on data.. Flynn’s . taxonomy (Michael Flynn, 1967) classifies computer architectures based on the number of instructions that can be executed and how they operate on data.. 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. Efficient and scalable architectures to perform pleasingly parallel, MapReduce and iterative data intensive computations on cloud environments. Thilina. . Gunarathne. (tgunarat@indiana.edu). Advisor : . Tim Bell. Charles Curran. Gordon . Lee. July 1. st. 2008. Bulk Repack Outlook. Bulk repack is much more difficult use case than repairing bad tapes or reclaiming holes. Time pressure. Repack to free slots or stop an old robot. Sunil Agarwal. Principal Program Manager, SQL Server. sunila@Microsoft.com. BRK4552. Definition and Value Prop. Operational Analytics with Disk-Based Tables. Operational Analytics with In-Memory OLTP. 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?. 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.. Cloud computing is a model for enabling . convenient. , . on-demand network access . to a . shared pool . of . configurable computing resources . (e.g., networks, servers, storage, applications, and services) [Mell_2009], [Berkely_2009]. . Ahmed . Sameh. and . Ananth. . Grama. NNSA/PRISM Center,. Purdue University. Path to . Exascale. Hardware Evolution. Key Challenges for Hardware. System Software. Runtime Systems. Programming Interface/ Compilation Techniques. 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 . 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.