'Data parallelism' diaporamas de présentation

Parallel Computing Explained

Parallel Computing Explained. Slides Prepared from the CI-Tutor Courses at NCSA http://ci-tutor.ncsa.uiuc.edu/ By S. Masoud Sadjadi School of Computing and Information Sciences Florida International University March 2009. Agenda. 1 Parallel Computing Overview 2 How to Parallelize a Code

2.8k views • 237 slides

Parallel Computing Explained

Parallel Computing Explained. Slides Prepared from the CI-Tutor Courses at NCSA http://ci-tutor.ncsa.uiuc.edu/ By S. Masoud Sadjadi School of Computing and Information Sciences Florida International University March 2009. Agenda. 1 Parallel Computing Overview 2 How to Parallelize a Code

2.81k views • 237 slides

CS4961 Parallel Programming Chapter 5 Introduction to SIMD Johnnie Baker January 14, 2011

CS4961 Parallel Programming Chapter 5 Introduction to SIMD Johnnie Baker January 14, 2011. Chapter Sources. J. Baker, Chapters 1-2 slides from Fall 2010 class in Parallel & Distributed Programming, Kent State University Mary Hall, Slides from Lectures 7-8 for CS4961, University of Utah.

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Supercomputing in Plain English Applications and Types of Parallelism

Supercomputing in Plain English Applications and Types of Parallelism. PRESENTERNAME PRESENTERTITLE PRESENTERDEPARTMENT PRESENTERINSTITUTION DAY MONTH DATE YEAR. Your Logo Here. Slides by Henry Neeman, University of Oklahoma. Outline. Monte Carlo: Client-Server N-Body: Task Parallelism

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Advanced Computer Architecture Data-Level Parallel Architectures

Advanced Computer Architecture Data-Level Parallel Architectures. Course 5MD00 Henk Corporaal December 2013 h.corporaal@tue.nl. This lecture. Data-level parallel architectures Vector machine SIMD sub-word parallelism support GPU Material: Book of Hennessy & Patterson

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Multithreaded Programming in Java

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Multithreaded Programming in Java

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Parallel and Distributed Systems in Machine Learning

Parallel and Distributed Systems in Machine Learning. Haichuan Yang. Outline. Background: Machine Learning Data Parallelism Model Parallelism Examples Future Trends. Background: Machine Learning.

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Building parallelized apps with .NET and Visual Studio

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Multi-level Selective Deduplication for VM Snapshots in Cloud Storage

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PlayStation2 as a General Purpose Computer

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Ian Foster Computation Institute Argonne National Laboratory & The University of Chicago

From the Heroic to the Logistical Programming Model Implications of New Supercomputing Applications. Ian Foster Computation Institute Argonne National Laboratory & The University of Chicago. With thanks to: Miron Livny , Ioan Raicu , Mike Wilde , Yong Zhao , and many others.

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Why Parallel/Distributed Computing

Why Parallel/Distributed Computing. Sushil K. Prasad sprasad@gsu.edu. What is Parallel and Distributed computing?. Solving a single problem faster using multiple CPUs E.g. Matrix Multiplication C = A X B Parallel = Shared Memory among all CPUs Distributed = Local Memory/CPU

301 views • 17 slides

Parallel Virtual Machine

Parallel Virtual Machine. Rama Vykunta. Introduction. PVM provides a unified frame work for developing parallel programs with the existing infrastructure PVM enables a collection of heterogeneous computer systems as a single parallel virtual machine Transparent to the user. Introduction.

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Post-processing analysis of climate simulation data using Python and MPI

Post-processing analysis of climate simulation data using Python and MPI. John Dennis ( dennis@ucar.edu ) Dave Brown ( dbrown@ucar.edu ) Kevin Paul ( kpaul@ucar.edu ) Sheri Mickelson ( mickelso@ucar.edu ). Motivation.

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Stream Processor Simulator

Stream Processor Simulator. Ben Gaudette Michael Pfeister CSE 520 Spring 2010. Project Description. Background Stream Processing is a paradigm that exploits parallel processing via data parallelism

364 views • 13 slides

Procedural Geometry Synthesis on the GPU

Procedural Geometry Synthesis on the GPU Patrick Lacz and John C. Hart University of Illinois, Urbana-Champaign

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Multicore for Science

Multicore for Science. Multicore Panel at eScience 2008 December 11 2008 Geoffrey Fox Community Grids Laboratory , School of informatics Indiana University gcf@indiana.edu , http://www.infomall.org. 1. Lessons.

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Near Real-time Data Assimilation for Aerosol Dispersion Model

Near Real-time Data Assimilation for Aerosol Dispersion Model Kostas Kalpakis , Yaacov Yesha , and Milt Halem , Shiming Yang @ CSEE.UMBC Bob Szabo , Eric Bouillet , and Lisa Amini @ IBM. Prediction of smoke dispersion. Meteorology data. Motivation. Data Assimilation. Experiments.

191 views • 4 slides