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Computing in Science & Engineering

Issue 4 • Date July-Aug. 2009

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Displaying Results 1 - 22 of 22
  • [Front cover]

    Page(s): c1
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  • CS Press [advertisement] for David Alan Grier's "Too Soon to Tell"

    Page(s): c2
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  • Table of contents

    Page(s): 1 - 2
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  • A Personal Reflection

    Page(s): 3
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  • Computational Science Research and Graduate Studies at San Diego State University

    Page(s): 5
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  • Computation in Modern Physics [review of Computation in Modern Physics, 3rd ed. (Gibbs, W.R., Ed.; 2006)]

    Page(s): 6 - 7
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  • Introducing Computing Now

    Page(s): 8 - 10
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  • Guest Editor's Introduction: Cloud Computing for the Sciences

    Page(s): 10 - 11
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  • SQL in the Clouds

    Page(s): 12 - 28
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    In a cloud computing context, the MapReduce algorithm comprises two massively parallel operations linked by a generic sorting and data-distribution process. Although this algorithm is the workhorse in most cloud computing strategies, it's a special case of a more general dataflow. In place of the two cloud operations, the proposed method substitutes longer sequences and then lets the user direct outputs to any subsequent downstream operation. However, the method retains the job-supervisor infrastructure, which performs the necessary sorting, collating, and distributing of these outputs prior to initiating operations. To evaluate SQL database queries, particularly those with correlated subqueries, a computation identifies and aligns data elements from widely separated storage locations, suggesting cloud algorithms that exploit the supervisory sorting process to achieve the desired alignments. Exploring such algorithms reveals that a few customizable templates, assembled recursively as necessary, can handle a wide class of SQL data-mining queries. View full abstract»

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  • Graph Twiddling in a MapReduce World

    Page(s): 29 - 41
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (922 KB)  

    As the size of graphs for analysis continues to grow, methods of graph processing that scale well have become increasingly important. One way to handle large datasets is to disperse them across an array of networked computers, each of which implements simple sorting and accumulating, or MapReduce, operations. This cloud computing approach offers many attractive features. If decomposing useful graph operations in terms of MapReduce cycles is possible, it provides incentive for seriously considering cloud computing. Moreover, it offers a way to handle a large graph on a single machine that can't hold the entire graph as well as enables streaming graph processing. This article examines this possibility. View full abstract»

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  • A High-Performance Computing Forecast: Partly Cloudy

    Page(s): 42 - 49
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    Cloud computing is emerging as an important computational resource allocation trend in commercial, academic, and industrial sectors. Yet, because the business model doesn't currently meet all the needs of high-performance computing (HPC)-the demands of capability computing, for example-the relationship between clouds and HPC suggests a partly cloudy forecast. View full abstract»

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  • Scientific Scripting for the Java Platform with jLab

    Page(s): 50 - 60
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    By modifying Groovy with Matlab-like constructs, the authors created a compiled mathematical scripting language called GroovySci for the jLab platform. The resulting code generation enhancements could ultimately extend Java's potential for scientific computing. View full abstract»

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  • Getting Started with GPU Programming

    Page(s): 61 - 64
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    This tutorial describes a step-by-step procedure for programming a Macintosh Nvidia GPU. General scientific programmers with some C knowledge can get started in parallel processing application development with relative ease. View full abstract»

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  • 3D Imaging and Simulation of Elastic Properties of Porous Materials

    Page(s): 65 - 73
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    Obtaining accurate estimates of elastic properties of disordered materials has been a problem of interest for decades. Recent advances in 3D computed tomography and numerical simulations have led to a new digital approach. View full abstract»

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  • A Virtual Platform for Auditory Organ Mechanics Analysis

    Page(s): 74 - 80
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    To better understand the relationship between the sound conduction system's structure and function, the authors developed a virtual platform that integrates 3D reconstruction and finite element modeling of the peripheral auditory organ to simulate biomechanical behavior of the middle and inner ear. View full abstract»

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  • IEEE Computer Society Membership [advertisement]

    Page(s): 81
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  • Postprocessing in Automated Grading Systems, Part 2

    Page(s): 82 - 85
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    The authors discuss a few simple grading algorithms for handling a wide array of homework and test questions that aren't otherwise handled well by the standard algorithm. View full abstract»

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  • The Promises of Functional Programming

    Page(s): 86 - 90
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    Adopting a functional programming style could make your programs more robust, more compact, and more easily parallelizable. However, mastering it requires some effort. This article's purpose is to explain what functional programming is and how it differs from traditional imperative programming. The author also explains how functional programming helps with concurrent and parallel programming. The language I use in the examples is Clojure, a modern dialect of Lisp. View full abstract»

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  • Trailblazing with Roadrunner

    Page(s): 91 - 95
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    The authors look at the changes occurring in computer system design, the rise of heterogeneous computing, and the effects these changes have on high-performance computing. View full abstract»

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  • Vets 1, Docs 1

    Page(s): 96
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  • Computing in Science & Engineering editorial calendar

    Page(s): c3
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  • IEEE Computer Society Career Center [advertisement]

    Page(s): c4
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Aims & Scope

Computing in Science & Engineering presents scientific and computational contributions in a clear and accessible format.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
George K. Thiruvathukal
Loyola University