parallel-computing-research-papers-2012-section-2
Introduction to parallel computing
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What is needed to begin the work.(Initialization) Who does what.(Work distribution) Access to work part.(Data/IO access) Whether they need info from each other to finish their own job.(Communication) When are they all done.(Synchronization) What needs to be
Introduction to parallel computing
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This presentation covers the basics of parallel computing . Beginning with a brief overview and some concepts and terminology associated with parallel computing , the topics of parallel memory architectures and programming models are then explored. These topics are
Sourcebook of parallel 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
The landscape of parallel computing research: A view from berkeley
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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. Conventional wisdom is
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
IVY: A Shared Virtual Memory System for Parallel Computing .
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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, and
NVIDIA CUDA software and GPU parallel computing architecture
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Page 1. NVIDIA CUDA Software and GPU Parallel Computing Architecture 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
DryadLINQ: A system for general-purpose distributed data- parallel computing using a high-level language
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DryadLINQ is a system and a set of language extensions that enable a new programming model for large scale distributed computing . It generalizes previous execution environments such as SQL, MapReduce, and Dryad in two ways: by adopting an expressive data model of Figure 2. Hex-connected systolic array for computing matrix multipli-cation [A] x [B]=[C]. can generally be divided into multiple special-purpose functional units, associative processors, array processors, data flow processors, functional programming language processors, and
Parallel computing on the Berkeley NOW
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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 provides an
Designing efficient algorithms for parallel computers
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Efficient Parallel Prefix Sum Algorithms for GPUs Ion T. C rstea, Parallel computing - an approach for scientific computin, Proceedings of the 5th WSEASIASME international conference on Engineering Designing Efficient Algorithms for Parallel Computers Mcgraw-Hill
Highly parallel computing
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Highly parallel computers: Perspectives Request Highly parallel machines represent a technology capable of providing superior performance for technical and commercial
Hypermeshes: Optical Intercomnnection Network for Parallel Computing
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A formal model for a class of optical mesh-based interconnec-tion networks called hypermeshes is proposed and characterized. Hypermeshes are based on the concept of orthogonal crossbar switches, with N nodes arranged in n-dimensional mesh structure
runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS
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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 used
Parallel computing and domain decomposition
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Domain decomposition techniques appear a natural way to make good use of parallel computers. In particular, these techniques divide a computation into a local part, which be done without any interprocessor communication, and a part that involves communication
High performance cluster computing : Architectures and systems (volume 1)
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706 31 MARS: An Adaptive Parallel Programming Environment 726 32 The Gardens Approach to Adaptive Parallel Computing 744 33 The ParPar System: A Software MPP 758 5 1.3 Towards Low Cost Parallel Computing and Motivations 7 1.4 Windows of Opportunity 9
Easier parallel computing in 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
Implicit schemes and parallel computing in unstructured grid CFD.
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The development of implicit schemes for obtaining steady state solutions to the Euler and Navier-Stokes equations on unstructured grids is outlined. Applications are presented that compare the convergence characteristics of various implicit methods. Next, the development
Instrumentation for future parallel computing systems
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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
The parallel computing laboratory at UC Berkeley: A research agenda based on the Berkeley view
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In December 200 we published a broad survey of the issues for the whole field concerning the multicore/manycore sea change (see view. eecs. berkeley. edu).(Asanovic, Bodik et al. 2006) We view the ultimate goal as the ability to create efficient and correct software
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