Chapter … Morgan Kaufmann Publishers 13 November, 2018 Chapter 4 — The Processor 1 Chapter 4 — The Processor — 140 Instruction-Level Parallelism (ILP) Pipelining: executing multiple instructions in parallel To increase ILP Deeper pipeline Less work per stage shorter clock cycle Multiple issue Replicate pipeline stages […]
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File Systems File Systems Anandha Gopalan (with thanks to D. Rueckert, P. Pietzuch, A. Tannenbaum and R. Kolcun) axgopala@imperial.ac.uk File System Objectives Long term non-volatile, online storage → e.g. programs, data, text, photos, music, . . . Sharing of information or software → e.g. editors, compilers, applications, . . . Concurrent access to shared data
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lec1 CS 314 Principles of Programming Languages Prof. Zheng Zhang Rutgers University Lecture 1: Overview and Basics September 5, 2018 Why study the principles of programming languages? 1. Better Understanding of Programming Language (PL) Theory • Programming language defines computation models tailored to think about and solve problems • It is believed that the
COMP 2013 Software Maintenance Workshop 01 (with answers) OO and Java Refresher (1/2) Peer-Olaf Siebers Please note that the slides published AFTER the lectures and workshops are the official slides and are the ones that should be used for revision. Topics • Lecture 1 – What is Software Maintenance? – Information about module organisation –
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Book Chapter 4 Concurrency: shared objects & mutual exclusion 1 ©Magee/Kramer 2nd Edition Chapter 4 Shared Objects & Mutual Exclusion Concurrency: shared objects & mutual exclusion 2 ©Magee/Kramer 2nd Edition Shared Objects & Mutual Exclusion Concepts: process interference. mutual exclusion. Models: model checking for interference modeling mutual exclusion Practice: thread interference in shared Java objects
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Com4521/Com6521: Parallel Computing with GPUs Assignment: Part 2 Deadline: Tuesday 15th May 2018 17:00 (week 12) Last Edited: 09/03/2018 Marking Assignment 2 (of 2) is worth 70% of the total assignment mark. The total assignment mark (both parts 1 and 2) is worth 80% of the total module mark. Assignment 2 marks will be weighted
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Chapter 1: Introduction COMP9313: Big Data Management Lecturer: Xin Cao Course web site: http://www.cse.unsw.edu.au/~cs9313/ 2.‹#› 1 Chapter 2: MapReduce I 2.‹#› What is MapReduce Origin from Google, [OSDI’04] MapReduce: Simplified Data Processing on Large Clusters Jeffrey Dean and Sanjay Ghemawat Programming model for parallel data processing Hadoop can run MapReduce programs written in various languages:
PowerPoint Presentation Parallel Computing with GPUs: Introduction to CUDA Dr Paul Richmond http://paulrichmond.shef.ac.uk/teaching/COM4521/ This lecture CUDA Programming Model CUDA Device Code CUDA Host Code and Memory Management CUDA Compilation and execution in Visual Studio Programming a GPU with CUDA Application Code GPU CPU Use GPU to Parallelize Compute-Intensive Functions Rest of Sequential CPU Code GPU
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Programming Foundations FIT9131 Testing & Debugging Week 7 FIT9131 Week 7 1 Programming Foundations FIT9131 Testing & Debugging Week 7 FIT9131 Week 7 2 Lecture outline •Software development and maintenance •Program errors – syntax, execution, logic •Testing •Unit testing (within BlueJ) •Designing and implementing a Test Strategy • needed for Assignment 2 •Regression testing •Debugging
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Microsoft PowerPoint – omp-hands-on-SC08 (2).ppt [Read-Only] 1 A “Hands-on” Introduction to OpenMP* Tim Mattson Principal Engineer Intel Corporation timothy.g.mattson@intel.com * The name “OpenMP” is the property of the OpenMP Architecture Review Board. Larry Meadows Principal Engineer Intel Corporation lawrence.f.meadows@intel.com 2 Preliminaries: part 1 Disclosures The views expressed in this tutorial are those of the people