Parallel computing - PowerPoint PPT Presentation
12.010 Computational Methods of Scientific Programming
12.010 Computational Methods of Scientific Programming Lecturers Thomas A Herring, Room 54-820A, tah@mit.edu Chris Hill, Room 54-1511, cnh@gulf.mit.edu Web page http://www-gpsg.mit.edu/~tah/12.010 Review of last Lecture Examined parallel computing and MPI implementation.
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Introduction to High Performance Computing: Parallel Computing, Distributed Computing, Grid Computing and More
Introduction to High Performance Computing: Parallel Computing, Distributed Computing, Grid Computing and More. Dr. Jay Boisseau Director, Texas Advanced Computing Center boisseau@tacc.utexas.edu December 3, 2001. Texas Advanced Computing Center. The University of Texas at Austin. Outline.
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How to Think Algorithmically in Parallel?
How to Think Algorithmically in Parallel?. Uzi Vishkin. Commodity computer systems. Chapter 1 1946 2003: Serial . 5KHz 4GHz. Chapter 2 2004--: Parallel . # ” cores ” : ~d y-2003 Apple 2004: 1 core 2008: 8 cores 2015: ?? cores Windows 7: scales to 256 cores…
By LionelDale
The eXplicit MultiThreading (XMT) Easy-To-Program Parallel Computer: A PRAM-On-Chip Proof-of-Concept
The eXplicit MultiThreading (XMT) Easy-To-Program Parallel Computer: A PRAM-On-Chip Proof-of-Concept. Uzi Vishkin (Lack of) ease-of-programming failed all parallel computers to date Vendors are yet to offer easy-to-program (& scalable) many-cores
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Parallel Computing @ ISDS
Parallel Computing @ ISDS. Chris Hans 29 November 2004. Organization. Basics of Parallel Computing Structural Computational Coding for Distributed Computing Examples Resources at Duke CSEM Cluster. Basics of Distributed Computing. Online Tutorial (Livermore Nat. Lab.)
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LHC Upgrade
LHC Upgrade. Kai Hock. The LHC Motivation and Activities The Science Case Technical Issues and Challenges Opportunities for us Scale of project UK support. Main Sources CERN LHC website - the LHC http://public.web.cern.ch/Public/en/LHC/LHC-en.html CARE-HHH website - LHC upgrade
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Cloud Application Development
Cloud Application Development. China Machine Press, China. McGraw Hill, India. Morgan Kauffman, USA. Unit 1- PART1: Fundamentals of Cloud Programming and Systems. Computing Platforms and Technologies, Eras of Computing .
By hideaki
Programming Multicore Processors
Programming Multicore Processors. Aamir Shafi High Performance Computing Lab http://hpc.seecs.nust.edu.pk. Serial Computation . Traditionally, software has been written for serial computation: To be run on a single computer having a single Central Processing Unit (CPU)
By sandra_john
Introduction to Parallel Computing
Introduction to Parallel Computing. Ananth Grama, Anshul Gupta, George Karypis, and Vipin Kumar To accompany the text ``Introduction to Parallel Computing'', Addison Wesley, 2003. Topic Overview . Motivating Parallelism Scope of Parallel Computing Applications
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GPU programming
GPU programming. Usman Roshan. Parallel computing. Why in a deep learning course? Some machine learning programs take a long time to finish. For example large neural networks and kernel methods.
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Parallel Computing 2007: Bring your own parallel application
Parallel Computing 2007: Bring your own parallel application. February 26-March 1 2007 Geoffrey Fox Community Grids Laboratory Indiana University 505 N Morton Suite 224 Bloomington IN gcf@indiana.edu. Intel’s Application Stack. Discussed here Rest mainly classic parallel computing.
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Clouds and Web2.0 Introduction
Clouds and Web2.0 Introduction. CTS08 Tutorial Hyatt Regency Irvine California May 19 2008 Geoffrey Fox, Marlon Pierce Community Grids Laboratory , School of informatics Indiana University http://www.infomall.org/multicore gcf@indiana.edu , http://www.infomall.org. 1.
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Hybrid Cloud and Cluster Computing Paradigms for Scalable Data Intensive Applications
Hybrid Cloud and Cluster Computing Paradigms for Scalable Data Intensive Applications. Judy Qiu xqiu@indiana.edu http://salsahpc.indiana.edu School of Informatics and Computing Indiana University. April 15, 2011 University of Alabama. Challenges for CS Research.
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Threads
Threads. Lecture 3. Some Definitions. Parallelism : degree to which a multiprocessor application achieves parallel execution Concurrency : Maximum parallelism an application can achieve with unlimited processors System Concurrency : kernel recognizes multiple threads of control in a program
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Instant-access cycle stealing for parallel applications requiring interactive response
Instant-access cycle stealing for parallel applications requiring interactive response. Paul Kelly (Imperial College) Susanna Pelagatti (University of Pisa) Mark Rossiter (ex-Imperial, now with Telcordia). Application scenario…. Workplace with fast LAN and many PCs
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Supercomputing in Plain English
Supercomputing in Plain English. Teaching High Performance Computing to Inexperienced Programmers Henry Neeman, University of Oklahoma Julia Mullen, Worcester Polytechnic Institute Lloyd Lee, University of Oklahoma
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Linking Programming models between Grids, Web 2.0 and Multicore
Linking Programming models between Grids, Web 2.0 and Multicore. Distributed Programming Abstractions Workshop NESC Edinburgh UK May 31 2007 Geoffrey Fox Computer Science, Informatics, Physics Pervasive Technology Laboratories Indiana University Bloomington IN 47401 gcf@indiana.edu
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Wildfire Distributed, Grid-Enabled Workflow Construction and Execution
Arun Krishnan, PhD Assistant Professor, Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan. Wildfire Distributed, Grid-Enabled Workflow Construction and Execution. Affordable HPC Commodity hardware assembled into Beowulf clusters Pooled hardware in Grids
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Enabling Large Scale Scientific Computations for Expressed Sequence Tag Sequencing over Grid and Cloud Computing Cluster
Enabling Large Scale Scientific Computations for Expressed Sequence Tag Sequencing over Grid and Cloud Computing Clusters. Sangmi Lee Pallickara, Marlon Pierce, Qunfeng Dong, Chin Hua Kong Indiana University, Bloomington IN, USA *Presented by Marlon Pierce.
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Presentation Overview
Presentation Overview. 1. Models of Parallel Computing The evolution of the conceptual framework behind parallel systems. 2. Grid Computing The creation of a structure within the parallel framework to facilitate efficient use of shared resources. 3. Cilk Language and Scheduler
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