PARALLEL COMPUTING-TECHNOLOGY


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Parallel computing is the Computer Science discipline that deals with the system architecture and software issues related to the concurrent execution of applications. It has been an area of active research interest and application for decades, mainly the focus of high performance computing, but is now emerging as the prevalent computing paradigm due to the semiconductor industry ™s shift to multicore processors

The landscape ofparallel computing RESEARCH PAPER: A view from berkeley
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Abstract The recent switch to parallel microprocessors is a milestone in the history of computing. Industry has laid out a roadmap for multicore designs that preserves the programming paradigm of the past via binary compatibility and cache coherence.

Knowledge frontiers: public sector research and industrial innovation in biotechnology, engineering ceramics andparallel computing
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Publication » Knowledge Frontiers: Public Sector Research and Industrial Innovation in Biotechnology, Engineering Ceramics, andParallel Computing . The Complexity Challenge: Technological Innovation for the 21st . Google Books Result Knowledge Frontiers: Public Sector

Sourcebook ofparallel computing
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Michael Mascagni Monte Carlo methods (MCMs) have been, and continue to be, very popular algorithms for solving a wide variety of problems in science, engineering, and technology. However, they are generally methods of last resort. As Mark Kác, a probability

IVY: A Shared Virtual Memory System forParallel Computing .
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Abstract A shared virtual memory system can provide a virtual address space shared among all processors in a loosely-coupled multiprocessor. This paper shows that such a memory can solve many problems in message passing systems on loosely-coupled multiprocessors,

Vector models for data- parallel computing
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This book is a revised version of my Doctoral Dissertation, which was completed at the Massachusetts Institute of Technology in November, 1988. The main purpose of the work was to explore the power of data-parallel programming; this exploration lead to the following

NVIDIA CUDA software and GPUparallel computingarchitecture
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Page 1. NVIDIA CUDA Software and GPUParallel ComputingArchitecture David B. Kirk, Chief Scientist Page 2. NVIDIA Corporation 2006-2008 2 Outline Applications of GPU Computing CUDA Programming Model Overview Programming in CUDA The Basics How to Get Started!

Parallel computingon the Berkeley NOW
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Abstract: The UC Berkeley Network of Workstations (NOW) project demonstrates a new approach to largescale system design enabled by technology advances that provide inexpensive, low latency, high bandwidth, scalable interconnection networks. This paper

Classical molecular dynamics andparallel computing
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Abstract: an overview is given about classical molecular dynamics computer simulation. The main ingredients which enter into a program are presented and special emphasis is put on parallelisation approaches. Also the limits and challenges of parallel algorithms are

Information Power Grid: The new frontier inparallel computing
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ASAs Information Power Grid is an example of an emerging, exciting concept that can potentially make high-performance computing power accessible to general users as easily and seamlessly as electricity from an electrical power grid. In the IPG system, high-

runjags: An R package providing interface utilities, model templates,parallel computingmethods and additional distributions for MCMC models in JAGS
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Abstract The runjags package provides a set of interface functions to facilitate running Markov chain Monte Carlo models in JAGS from within R. Automated calculation of appropriate convergence and sample length diagnostics, user-friendly access to commonly

A micro/macro approach forparallel computingof heterogeneous structures
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Abstract A new micro/macro computational strategy is proposed for the analysis of structures which are described up to themicrolevel, such as composite structures. This strategy is intended to overcome standard homogenization techniques coupled with a local re-analysis,

Easierparallel computingin R with snowfall and sfCluster
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Many statistical analysis tasks in areas such as bioinformatics are computationally very intensive, while lots of them rely on embarrassingly parallel computations (Grama et al., 2003). Multiple computers or even multiple processor cores on standard desktop computers,

Introduction toparallel computing
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Processor (CPU) is the active part of the computer, which does all the work of data manipulation and decision making.Datapath is the hardware that performs all the required data processing operations, for example, ALU, registers, and internal buses.Control is the

MiniGhost: a miniapp for exploring boundary exchange strategies using stencil computations in scientificparallel computing
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Abstract A broad range of scientific computation involves the use of difference stencils. In a parallel computingenvironment, this computation is typically implemented by decomposing the spacial domain, inducing ahalo exchangeof process-owned boundary data. This

Microscopic traffic simulation for att systems analysis. aparallel computingversion
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Abstract The rapid growing of applications of Advanced Transport Telematic (ATT) systems in recent years is generating an increasing request for tools to help in system design and assessment. Microscopic traffic simulation has proven to be one of the most useful tools to

Optimizing Data Partitioning for Data- Parallel Computing .
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Abstract Performance of data- parallel computing(eg, MapReduce, DryadLINQ) heavily depends on its data partitions. Solutions implemented by the current state of the art systems are far from optimal. Techniques proposed by the database community to find optimal data

Instrumentation for futureparallel computingsystems
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Parallel systems pose a unique challenge to performance measurement and instrumentation. The complexity of these systems manifests itself as an increase in performance complexity as well as programming complexity. The complex interaction of the

Image processing tasks usingparallel computingin multi core architecture and its applications in medical imaging
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Abstract: To find accuratereliable result in image analysis, it is important that image is processed and analyzed using image processing suitable AI technique further at the same time it is highly desired that processing time must be minimum. Preprocessing of the image

Parallel computingfor finite element models of surface water flow
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In recent years, there has been much interest in the numerical solution to shallow water equations. Simulation of shallow water systems can serve numerous purposes, including modeling tidal fluctuations and computing tidal ranges and surges such as tsunamis and

Parallel computing : Performance metrics and models
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Abstract We review the many performance metrics that have been proposed for parallel systems (ie, program architecture combinations). These include the many variants of speedup, e ciency, and isoe ciency. We give reasons why none of these metrics should be

Teachingparallel computingto freshmen
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Abstract Parallelism is the future of computing and computer science and should therefore be at the heart of the CS curriculum. Instead of continuing along the evolutionary path by introducing parallel computation\top down(rst in special junior-senior level courses), we are % Eve y technical field has its own A specialized terms. Understanding the jargon is essential to understanding the literature. adaptive:(a~ $) Taking local surroundings or history into account. An adaptive mesh-generating algorithm generates a finer mesh near

Application ofparallel computingto the simulation of forest fire propagation
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ABSTRACT The advantages ofparallel computinghave been shown in several simulation fields. The main and obvious advantage ofparallel computingis that it offers an increase in the speed of the simulation and, therefore, a reduction in the overall simulation time. The

Parallel and distributed computing handbook
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862 Chapter 30. Tools for Portable High-PerformanceParallel Computing865 Doreen Y. Cheng 30.1 Introduction 865 Scientific Computation 981 Timothy G. Mattson 34.1 Programming Models forParallel Computing982 34.2 Algorithms for Parallel Scientific Computing 983

A hypercube-based scalable interconnection network for massivelyparallel computing
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Abstract-An important issues in the design of interconnection networks for massively parallel computers is scalability. A new scalable interconnection network topology, called Double- Loop Hypercube (DLH), is proposed. The DLH network combines the positive features of the

Point to point processing of digital images usingparallel computing
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Abstract This paper presents an approach the point to point processing of digital images usingparallel computing particularly for grayscale, brightening, darkening, thresholding and contrast change. The point to point technique applies a transformation to each pixel on

Efficientparallel computingfor solving linear systems of equations
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Abstract Linear systems of equations are common throughout the disciplines of science. The conjugate gradient method is a common iterative method used to solve systems with symmetric positive definite system matrices. With a matrix-free implementation, the method is

Parallel computingin industry {an initial survey
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Parallel Computingin Industry An Initial Survey Geoffrey C. Fox Syracuse University Northeast Parallel Architectures Center Syracuse, New York 13244-4100 gcfGnova.npac.syr.edu Abstract We describe some issues in and examples of the use of high performance computing in industry

Improvement in computational efficiency for HCCI engine modeling by using reduced mechanisms andparallel computing
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Abstract Detailed chemistry was used with engine CFD code for HCCI engine combustion modeling in order to achieve a more accurate analysis. The present study improved the computational efficiency by using reduced mechanisms andparallel computingschemes. In

Distributed andparallel computingin MADM domain using the OPTCHOICE software
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Abstract:-The paper presents a method for solving the general Multi-Attribute Decision Making (MADM) problems, by distributed andparallel computing with the OPTCHOICE software. One presents the scheduling and load balancing algorithm for concurrent solving

Spatial interpolation in massivelyparallel computingenvironments
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ABSTRACT Prediction of environmental phenomena at non-observed locations is a fundamental task in geographic information science. Often, samples are taken at a limited number of sensor locations and spatial and spatio-temporal interpolation is used to generate

Reflections on failure in post-terascaleparallel computing
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Page 1. Reflections on Failure in Post-TerascaleParallel Computing2007 Int. Conf. on Parallel Processing, XiAn China Garth Gibson Carnegie Mellon University and Panasas Inc. DOE SciDAC Petascale Data Storage Institute (PDSI), www.pdsi-scidac.org w/ Bianca Schroeder, Carnegie

Shape analysis forparallel computing
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Abstract Shapes are also known as (data) structures, containers, or indexing systems. They are used extensively inparallel computingto determine communication strategies, load- balancing, evaluation strategies, etc. Although widely used, most applications of shape are

MRPC: A high performance RPC system for MPMDparallel computing
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Abstract MRPC is an RPC system that is designed and optimized for MPMDparallel computing . Existing systems based on standard RPC incur an unnecessarily high cost when used on high-performance multi-computers, limiting the appeal of RPC-based languages in

A survey of models forparallel computing
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Parallel computingtechnologies
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The PaCT-2013 ( Parallel ComputingTechnologies) conference was a four-day event held in St. Petersburg. This was the 12th international conference in the PaCT series. The conferences are held in Russia every odd year. The first conference, PaCT-91, was held in

Introduction toParallel Computing
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Page 1. Introduction toParallel ComputingGeorge Karypis Principles of Parallel Algorithm Design Page 2. OutlineOverview of some Serial AlgorithmsParallel Algorithm vs Parallel Formulation Elements of a Parallel Algorithm/FormulationCommon Decomposition Methods

Java on networks of workstations (JavaNOW): aparallel computingframework inspired by Linda and the Message Passing Interface (MPI)
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Abstract Networks of workstations are a dominant force in the distributed computing arena, due primarily to the excellent price/performance ratio of such systems when compared to traditionally massively parallel architectures. It is therefore critical to develop programming

Parallel computingthe Longest Common Subsequence (LCS) on GPUs: efficiency and language suitability
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AbstractSequence alignment is one of the most used tools in bioinformatic to find the resemblance among many sequences like ADN, ARN, amino acids. The longest common subsequence (LCS) of biological sequences is an essential and effective technique in

Avalanche: A communication and memory architecture for scalableparallel computing
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Abstract As the gap between processor and memory speeds widens, system designers will inevitably incorporate increasingly deep memory hierarchies to maintain the balance between processor and memory system performance. At the same time, most

Parallel ComputingTechnologies 4th International Conference Pact 97 Yaroslavl Russia September
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Thank you for readingparallel computingtechnologies 4th international conference pact 97 yaroslavl russia september. Maybe you have knowledge that, people have search numerous times for their chosen novels like thisparallel computingtechnologies 4th international

Linear algebraic primitives forparallel computingon large graphs
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Abstract We review and evaluate storage formats for sparse matrices in the light of the key primitives that are useful for implementing graph algorithms on them. We present complexity results of these primitives on different sparse storage formats both in the RAM model and in

The 2nd international conference on vector andparallel computing : Issues in applied research and development
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In the summer of 1986, an international conference on supercomputing was held at Loen, Norway [10, 7]. On June 6-10, 1988, a successor conference took place at Tromso and was named LoenII by the organizing committee. About 300 participants from 23 countries

Visual programming andparallel computing
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Abstract Visual programming arguably provides greater benefit in explicit parallel programming, particularly coarse grain MIMD programming, than in sequential programming. Explicitly parallel programs are multi-dimensional objects; the natural

Enhancing multicore system performance usingparallel computingwith MATLAB
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Each dot represents an eigenvalue on a complex plain. Notice that most of the eigenvalues reside inside a circle of radius 1 and are centered at the origin of the axisa compelling indication that, in accordance with Girkos circular law, the magnitude of an eigenvalue does

Real time target tracking with binary sensor networks andparallel computing
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AbstractA parallel real time data fusion and target tracking algorithm for very large binary sensor networks is presented. A binary sensor can give an on or off signal to indicate the presence or absence of targets within its range, but it cannot tell how many targets are

Beowulf A New Hope forParallel Computing
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Abstract The Beowulf model for clusters of commodity computers [15, 17, 18] has become very popular over the last year, particularly amongst university research groups and other organisations less able to justify large procurements. The Beowulf concept is usually applied

Massivelyparallel computingand factoring
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Let n be an odd positive composite integer that is not a prime power. It is well known that it is usually easy to prove ns compositeness without revealing any information about its factors . It is widely believed that factoring n is, in general, a hard problem. Indeed, if n has 200

Parallel computingfocussing on multiprocessing with shared memory; problems when developing parallel programs with respect to different memory
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Page 1. December 2012 Seminar MSE: Foundation Of Multicore Models Version 1.0Parallel computingfocussing on multiprocessing with shared memory; problems when developing parallel programs with respect to different One of the main aspects to be recognized is the fact that a

Distributed Generation Placement Design and Contingency Analysis withParallel ComputingTechnology.
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AbstractDistributed Generation (DG) is a promising solution to many power system problems such as voltage regulation, power loss, etc. The location in the power system for DG placement is found to be very important. The additional DG placement strategy is also

Parallelization of the NASA Goddard cumulus ensemble model for massivelyparallel computing
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Cloud-resolving models (CRMs), which are based on the non-hydrostatic equations of motion, have been extensively applied to cloud-scale and mesoscale processes during the past four decades (see a brief review by Tao 2003). Table 1 lists the major foci and some

Introduction toParallel Computing
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Page 1.Jesper Larsson Träff WS17/18 Introduction toParallel ComputingShared-memory systems and programming Jesper Larsson Träff traff@par.tuwien.ac.at TU WienParallel ComputingPage 2.Jesper Larsson Träff WS17/18 Shared-memory architecturesmachines

Improved performance of advance encryption standard usingparallel computing
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ABSTRACT This paper presents the implementation of Advance encryption (AES) algorithm usingparallel computing . Most of the research for improving performance of AES is based on hardware implementation. This paper presents the parallel implementation of AES using Data-driven computation and dataflow computer architecture show promise as a means of achieving high-speed computing through exploitation of software parallelism. Centres of expertise in dataflow techniques have emerged at MIT in the US, CERT-ONERA in France,

Programming environments forparallel computing : A comparison of cps, linda, p4, pvm, posybl, and tcgmsg
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Abstract In this paper, six portable parallel programming environments are compared. Results for two node communication benchmarks are reported for each environment. Results for the more complicated four node communications tests are reported for all of the

Soma-workflow: a unified and simple interface toparallel computingresources
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Abstract.Parallel computingresources are now available everywhere, from a simple multiple core laptop to sophisticated clusters and grids. Soma-workflow originated in the observation that many computational processes in neuroimaging are highly parallel by

Integrating parallel and distributed computing into undergraduate courses at all levels
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Building on background information in PDC presented in CS1: review of parallelism core concepts of multi-processing and terminology to reinforce the foundations presented in CS1. Expanding awareness of common aspects ofparallel computing : usingparallel computing

Development of parallel climate/forecast models on 100 GFlops PARAM computing systems
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and SKDASH Indian Institute of Technology, Delhi, India ABSTRACT The Climate Models T211 T42 and Forecast Models T80 I T126 are ported on theParallel Computingsystem PARAM10000 PGSela, Weather Forecasting on Parallel Architecture,Parallel Computing -SOFTWARE SALES SERVICE-https://www.engpaper.net--