1. Parallel Computing - Wikipedia, The Free Encyclopedia Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can http://en.wikipedia.org/wiki/Parallel_computing

Parallel computing From Wikipedia, the free encyclopedia Jump to: navigation search Programming paradigms Agent-oriented ... Declarative (contrast: Imperative Constraint Functional Dataflow ... Function-level (contrast: Value-level Imperative (contrast: Declarative Procedural Language-oriented Domain-specific ... Non-structured (contrast: Structured Array (contrast: Scalar Iterative Nondeterministic Parallel computing Process-oriented Programming in the large and programming in the small Structured (contrast: Non-structured Modular (contrast: Monolithic) Recursive Object-oriented Automata-based By separation of concerns Aspect-oriented Subject-oriented Role-oriented ... Value-level (contrast: Function-level This box: view talk edit Parallel computing is a form of computation in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently ("in parallel"). There are several different forms of parallel computing: bit-level instruction level data , and task parallelism . Parallelism has been employed for many years, mainly in

2. Parallel Computing Parallel computing. Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain faster results. http://www.fact-index.com/p/pa/parallel_computing.html

Main Page See live article Alphabetical index Parallel computing Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain faster results. The term parallel processor is sometimes used for a computer with more than one central processing unit , available for parallel processing. Systems with thousands of such processors are known as massively parallel There are many different kinds of parallel computer (or "parallel processor"). They are distinguished by the kind of interconnection between processors (known as "processing elements" or PEs) and between processors and memory. Flynn's taxonomy also classifies parallel (and serial) computers according to whether all processors execute the same instructions at the same time ( single instruction/multiple data SIMD ) or each processor executes different instructions ( multiple instruction/multiple data MIMD While a system of n parallel processors is not more efficient than one processor of n times the speed, the parallel system is often cheaper to build. Therefore, for tasks which require very large amounts of computation and/or have time constraints on completion, parallel computation is an excellent solution. In fact, in recent years, most high performance computing systems, also known as supercomputers, have a parallel architecture.

3. Parallel Computing - Simple English Wikipedia, The Free Encyclopedia Parallel computing is a form of computation in which many instructions are carried out simultaneously (termed in parallel ), depending on the theory that large problems can often be http://simple.wikipedia.org/wiki/Parallel_computing

Parallel computing From Wikipedia, the free encyclopedia Jump to: navigation search Parallel computing is a form of computation in which many instructions are carried out simultaneously (termed "in parallel"), depending on the theory that large problems can often be divided into smaller ones, and then solved concurrently ("in parallel"). There are several different forms of parallel computing: Bit-level parallelism Instruction-level parallelism Data parallelism Task parallelism It has been used for many years, mainly in high-performance computing , with a great increase in its use in recent years, due to the physical constraints preventing frequency scaling . Parallel computing has become the main model in computer architecture , mainly in the form of multicore processors However, in recent years, power consumption by parallel computers has become a concern. Parallel computers can be classified according to the level at which the hardware supports parallelism—with multi-core and multi-processor computers having multiple processing elements inside a single machine, while

4. Parallel Computing -- CFD-Wiki, The Free CFD Reference Introduction . Ever heard of Divide and Conquer ? Ever heard of Together we stand, divided we fall ? This is the whole idea of parallel computing. http://www.cfd-online.com/Wiki/Parallel_computing

Home News Index Post News ... Wiki Parallel computing From CFD-Wiki Jump to: navigation search Contents Introduction Types of parallel computers Shared memory multiprocessor Distributed memory multicomputer ... Resources Introduction Ever heard of "Divide and Conquer"? Ever heard of "Together we stand, divided we fall"? This is the whole idea of parallel computing. A complicated CFD problem involving combustion, heat transfer, turbulence, and a complex geometry needs to be tackled. The way to tackle it is to divide it and then conquer it. The computers unite their efforts to stand up to the challenge! Parallel computing is defined as the simultaneous use of more than one processor to execute a program. This formal definition holds a lot of intricacies inside. For instance, given a program, one cannot expect to run this program on a 1000 processors without any change to the original code. The program has to have instructions to guide it to run in parallel. Since the work is shared or distributed amongst "different" processors, data has to be exchanged now and then. This data exchange takes place using different methods depending on the type of parallel computer used. For example, using a network of PCs, a certain protocol has to be defined (or installed) to allow the data flow between PCs. The sections below describe some of the details involved. Types of parallel computers There are two fundamental types of parallel computers A single computer with multiple internal processors, known as a

5. Parallel Computing: Facts, Discussion Forum, And Encyclopedia Article Computing, also known as computer science, is usually defined as the activity of using and improving computer technology, computer hardware and software. http://www.absoluteastronomy.com/topics/Parallel_computing

Home Discussion Topics Dictionary ... Login Parallel computing Parallel computing Topic Home Discussion Discussion Ask a question about ' Parallel computing Start a new discussion about ' Parallel computing Answer questions from other users Full Discussion Forum Encyclopedia Parallel computing is a form of computation Computing Computing, also known as computer science, is usually defined as the activity of using and improving computer technology, computer hardware and software. It is the computer-specific part of information technology... in which many calculations are carried out simultaneously, operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently Concurrency (computer science) In computer science, concurrency is a property of systems in which several computations are executing simultaneously, and potentially interacting with each other... ("in parallel"). There are several different forms of parallel computing: bit-level Bit-level parallelism Bit-level parallelism is a form of parallel computing based on increasing processor word size. From the advent of very-large-scale integration computer chip fabrication technology in the 1970s until about 1986, advancements in computer architecture were done by increasing bit-level... instruction level Instruction level parallelism Instruction-level parallelism is a measure of how many of the operations in a computer program can be performed simultaneously. Consider the following program: 1. e = a + b 2. f = c + d 3. g = e * f...

6. Parallel Computing Q Any example of parallel computing in personal computers? Can anyone give me an example where parallel computing (to be more precise pipelining) is used in personal http://www.kosmix.com/topic/Parallel_computing

7. Parallel Computing File Format Microsoft Powerpoint View as HTML http://www.cs.ucf.edu/courses/cot4810/fall04/presentations/Parallel_Computing.pp

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8. Parallel Computing - Definition Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain faster results. http://www.wordiq.com/definition/Parallel_computing

Parallel computing - Definition Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain faster results. Contents showTocToggle("show","hide") 1 Parallel computing systems 2 Parallel software 3 Topics in parallel computing 4 See also: ... 6 External links: Parallel computing systems The term parallel processor is sometimes used for a computer with more than one central processing unit , available for parallel processing. Systems with thousands of such processors are known as massively parallel There are many different kinds of parallel computers (or "parallel processors"). They are distinguished by the kind of interconnection between processors (known as "processing elements" or PEs) and between processors and memories. Flynn's taxonomy also classifies parallel (and serial) computers according to whether all processors execute the same instructions at the same time ( single instruction/multiple data SIMD ) or each processor executes different instructions ( multiple instruction/multiple data MIMD ). Parallel processor machines are also divided into symmetric and asymmetric multiprocessors, depending on whetever all the processors are capable of running all the operating system code and say acessing I/O devices or if some processors are more or less priviledged.

9. Introduction To Parallel Computing And Cluster Computers This is a very basic introduction to the world of parallel computing. I ve tried to provide all the information needed to get started. http://www.scl.ameslab.gov/Projects/parallel_computing/

Introduction to Parallel Computing and Cluster Computers Dave Turner - Ames Laboratory turner@amelab.gov http://www.scl.ameslab.gov/Projects/parallel_computing/ This is a very basic introduction to the world of parallel computing. I've tried to provide all the information needed to get started. There are also links to more additional information on more advanced topics. The last part is a basic introduction to designing and building cluster computers. Types of parallel computers Parallel vector processors Shared-memory multiprocessors Distributed-memory MPP's ... C-Plant: Alpha cluster Links to more advanced topics MP_Lite message-passing library documentation MPI books and online tutorials Ames Laboratory Condensed Matter Physics ... ISU Physics

10. Supercomputing And Parallel Computing Research Groups Academic research groups and projects related to parallel computing. http://www.cs.cmu.edu/~scandal/research-groups.html

Supercomputing and Parallel Computing Research Groups Index Conferences Vendors Supercomputers Note: I've moved on to another job , and I can no longer afford the time to keep this site updated. I recommend IEEE's ParaScope as a good alternative. Academic research groups working in the field of supercomputing and parallel computing. ABCPL An object-Based Concurrent Language. Model of concurrency based on parallel active objects. Adl Data parallel functional programming languages for distributed memory architectures. Adsmith Object-Based DSM Environment on PVM. Alewife Large-scale multiprocessor with shared memory and message passing. AMDC Active-Message Driven Computing. Computational model and system based on computation at abstract locations, and built on active messages. AM Active Messages. Simple primitives exposing full hardware performance to higher communication layers. APAR Parallel Architectures group. Portable parallel programming environment, parallel 3D terrain analysis and visualization. APE/Quadrics High performance simulations of lattice gauge theories and QCD.

11. Parallel Computing Developer Center Parallel Programming with Microsoft .NET Design Patterns for Decomposition and Coordination on Multicore Architectures This book describes patterns for parallel programming http://msdn.microsoft.com/en-us/concurrency/default.aspx

12. Parallel Computing Toolbox - MATLAB Parallel Computing Toolbox enables you to harness a multicore computer, GPU, cluster, grid, or cloud to solve computationally and dataintensive problems. The toolbox provides http://www.mathworks.com/products/parallel-computing/

var host_pre = "http://www"; Home Select Country Contact Us Store Search Create Account Log In Solutions Academia ... Installation Instructions Other Parallel Computing Resources Technical Literature User Stories Parallel Computing Toolbox Perform parallel computations on multicore computers, GPUs, and computer clusters Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB applications without CUDA or MPI programming. You can use the toolbox with Simulink to run multiple simulations of a model in parallel. MATLAB GPU Support The toolbox provides eight workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server ™). You can run parallel applications interactively or in batch. Parallel Computing with MATLAB on Amazon Elastic Compute Cloud (EC2) Parallel Computing Toolbox Key Features Programming Parallel Applications Using Built-In Parallel Algorithms in Other MathWorks Products ... Scaling Up to Clusters, Grids, and Clouds Using MATLAB Distributed Computing Server

13. Parallel Computing Works A book about parallel computing, focusing on a few specific research projects done at Caltech. http://www.netlib.org/utk/lsi/pcwLSI/text/

Next: Contents Parallel Computing Works This book describes work done at the Caltech Concurrent Computation Program , Pasadena, Califonia. This project ended in 1990 but the work has been updated in key areas until early 1994. The book also contains links to some current projects. Geoffrey C. Fox Roy D. Williams Paul C. Messina ISBN 1-55860-253-4 Morgan Kaufmann Publishers , Inc. 1994 ordering information What is Contained in Parallel Computing Works? We briefly describe the contents of this book Applications The heart of this work is a set of applications largely developed at Caltech from 1985-1990 by the Caltech Concurrent Computation Group. These are linked to a set of tables and Glossaries Application Paradigms in High Performance Fortran Algorithms and Applications in High Performance Fortran Petaflop Application Categories developed at a workshop. A table developed by Fox in 1991-1993 to categorize New York State Industrial Applications of HPCC Applications are classified into 5 problem classes: Synchronous Applications , more in I and II Such applications tend to be regular and characterised by algorithms employing simultaneous identical updates to a set of points, more in

14. ScienceDirect - Parallel Computing, Volume 36, Issue 12, Pages 645-712 (December The online version of Parallel Computing on ScienceDirect, the world s leading platform for high quality peerreviewed full-text publications in science, http://www.sciencedirect.com/science/journal/01678191

Hub ScienceDirect Scopus Register ... Go to SciVal Suite Username: Password: Remember me Not Registered? Forgotten your username or password? Go to Athens / Institution login Home ... Help All fields Author Advanced search Journal/Book title Volume Issue Page Search tips Parallel Computing Sample Issue Online About this Journal Submit your Article Shortcut link to this Title ... New Article Feed Signed up for new Volumes / Issues [ remove Alert me about new Volumes / Issues Your selection(s) could not be saved due to an internal error. Please try again. Added to Favorites [ remove Add to Favorites Font Size: Add to my Quick Links Volume 36, Issue 12, Pages 645-712 (December 2010) = Full-text available = Abstract only Articles in Press Volume 36 (2010) Volume 36, Issue 12 - selected pp. 645-712 (December 2010) Volume 36, Issues 10-11 pp. 553-644 (October-November 2010) Parallel Architectures and Bioinspired Algorithms Volume 36, Issue 9 pp. 487-552 (September 2010) Volume 36, Issue 8

15. Nan's Parallel Computing Page Links to online books, tutorials, and research projects. http://www.cs.rit.edu/~ncs/parallel.html

Nan's Parallel Computing Page This list contains links related to parallel computing. If you have any suggestions please send me e-mail: Please note that an ' XXX ' at the end of a line means that I have recently (see the date at bottom of page) had trouble getting there. I am working on fixing/deleting these links. Bibliographies Online Books Cluster Computing Colgate UFE 1998 Workshop Links ... Nan Schaller's Interests Page e-mail: Nan Schaller's Home Page RIT's Home Page Links Go Parallel Computers Odyssey FAQ The sC++ language - Synchronous Java Applied Parallel Research, Inc. ARCH Library ... Tools for CSP Cluster Computing MOSIX - Scalable Cluster Computing for Linux (Hebrew Univ.) Appleseed-parallel Macintosh Cluster IEEE CS Task Force on Cluster Computing Kal�ka, Distributed system for high performance parallel computing (Univ. of Auckland, NZ) SCL Cluster Cookbook Java for Parallel/High Performance Computing Concordia Home Page Concurrent Programming Using the Java Language concurrency: State Models and Java Programs Infosphere Infrastructure - Current Release ... CTJ - Communicating Threads in Java (NL) Java(tm) Distributed Computing Java Parallel JavaOne Session Presentations TOPIC Links and Pages ... http://wwwipd.ira.uka.de/JavaParty/

16. Internet Parallel Computing Archive Links to information about parallel algorithms, computing environments and tools, newsgroups, and general references. http://wotug.ukc.ac.uk/parallel/

Hosted by WoTUG at Computer Science Department University of Kent at Canterbury , UK Edited 1993-2000 by Dave Beckett News IPCA Mirrors Add Search Mail ... Environments and Systems LAM Hardware Vendors Languages ... OSes Topical Information Events Jobs IPCA Information Usage ... occam language Overview Occam For All project; Compilers: KRoC SPOC SGS-Thompson ... TDS and Toolset ; Docs: libraries Reference Acronyms Biblios Books Consultants ... Transputer processor Software Bibliographies DS-Links Networks, Routers and Transputers ... Article Archive and Crisis in HPC Workshop Southampton Belfast Cardiff ... WoTUG and NATUG WoTUG 22 conference Biblios NATUG ... Other lists Last Modified: 1st March 2000 Dave Beckett and WoTUG

17. CRAN Task View: High-Performance And Parallel Computing With R This CRAN task view contains a list of packages, grouped by topic, that are useful for highperformance computing (HPC) with R. In this context, we are defining 'high http://cran.r-project.org/web/views/HighPerformanceComputing.html

CRAN Task View: High-Performance and Parallel Computing with R Maintainer: Dirk Eddelbuettel Contact: Version: This CRAN task view contains a list of packages, grouped by topic, that are useful for high-performance computing (HPC) with R. In this context, we are defining 'high-performance computing' rather loosely as just about anything related to pushing R a littler further: using compiled code, parallel computing (in both explicit and implicit modes), working with large objects as well as profiling. Unless otherwise mentioned, all packages presented with hyperlinks are available from CRAN, the Comprehensive R Archive Network. Several of the areas discussed in this Task View are undergoing rapid change. Please send suggestions for additions and extensions for this task view to the task view maintainer Suggestions and corrections by Achim Zeileis, Markus Schmidberger, Martin Morgan, Max Kuhn, Tomas Radivoyevitch, Jochen Knaus, Tobias Verbeke, Hao Yu, David Rosenberg, Marco Enea, Ivo Welch and Jay Emerson are gratefully acknowledged. Parallel computing: Explicit parallelism Several packages provide the communications layer required for parallel computing. The first package in this area was

18. Parallel Computing Nov 5, 2008 Parallel computing simply means using more than one computer processor to solve a problem. Classically, computers have been presented to http://cnx.org/content/m18099/latest/

var portal_url="http://cnx.org"; Skip to content Skip to navigation Log In Contact Us ... Report a Bug Search: Connexions Home Selected: Content Lenses About Us Help ... MyCNX You are here: Home Content » Parallel Computing Navigation Lenses What is a lens? Definition of a lens Lenses A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust. What is in a lens? Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content. Who can create a lens? Any individual member, a community, or a respected organization. What are tags? Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens. This content is ... Endorsed by What does "Endorsed by" mean This content has been endorsed by the organizations listed. Click each link for a list of all content endorsed by the organization. HPC Open Edu Cup Tags Supporting-material This module is included in Lens: High Performance Computing Open Education Cup 2008-2009 By: Ken Kennedy Institute for Information Technology As a part of collection: "2008-'09 Open Education Cup: High Performance Computing" Click the "HPC Open Edu Cup" link to see all content they endorse.

19. IS PARALLEL COMPUTING DEAD? Article about the future of the parallel computing industry. http://www.crpc.rice.edu/newsletters/oct94/director.html

Volume 7, Issue 1 - Spring/Summer 1999 Volume 6, Issue 3 Fall 1998 ... January 1993 IS PARALLEL COMPUTING DEAD? Ken Kennedy, Director, CRPC Is parallel computing really dead? At the very least, it is undergoing a major transition. With the end of the cold war, there is less funding for defense-oriented supercomputing, which has been the traditional mainstay of the high-end market. If parallel computing is to survive in the new environment, a much larger fraction of sales must be to industry, which seems to be substantially less concerned with high-end performance. Two factors bear on the size of the industrial market for parallel computing. First, most engineering firms have recently made the transition away from mainframes to workstations. These companies believe that if they need more computational power than they have on a single workstation, they should be able to get it by using a network of such machines. Whether or not this is true, it has substantially affected sales of tightly coupled parallel systems and must be taken into account when analyzing the needs of industry users. A second factor affecting commercial sales of parallel computer systems has been the reluctance of independent software vendors like MacNeal- Schwendler to move their applications to parallel machines. I believe the primary reason for this reluctance has been the absence of an industry standard interface that supports machine-independent parallel programming. Without such a standard, a software vendor's investment in conversion to parallel machines is not protectedwhen a new parallel computing architecture with a new programming interface emerges, the application would need to be retargeted.

20. Introduction To Parallel Computing Implicit Parallelism Trends in Microprocessor Architectures; Limitations of Memory System Performance; Dichotomy of Parallel Computing Platforms http://www-users.cs.umn.edu/~karypis/parbook/

Introduction to Parallel Computing. Ananth Grama, Purdue University, W. Lafayette, IN 47906 (ayg@cs.purdue.edu Anshul Gupta, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598 (anshul@watson.ibm.com George Karypis, University of Minnesota, Minneapolis, MN 55455 (karypis@cs.umn.edu Vipin Kumar, University of Minnesota, Minneapolis, MN 55455 (kumar@cs.umn.edu Follow this link for a recent review of the book published at IEEE Distributed Systems Online. Solutions to Selected Problems The solutions are password protected and are only available to lecturers at academic institutions. Click here to apply for a password. Click here to download the solutions (PDF File). Table of Contents ( PDF file PART I: BASIC CONCEPTS 1. Introduction (figures: [ PDF PS Motivating Parallelism Scope of Parallel Computing Organization and Contents of the Text 2. Parallel Programming Platforms (figures: [ PPT PDF PS (GK lecture slides [ PDF ]) (AG lecture slides [ PPT PDF PS Implicit Parallelism: Trends in Microprocessor Architectures Limitations of Memory System Performance Dichotomy of Parallel Computing Platforms Physical Organization of Parallel Platforms Communication Costs in Parallel Machines Routing Mechanisms for Interconnection Networks Impact of Process-Processor Mapping and Mapping Techniques Bibliographic Remarks 3. Principles of Parallel Algorithm Design (figures: [

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