• Login
    View Item 
    •   Eurographics DL Home
    • Eurographics Workshops and Symposia
    • EGPGV: Eurographics Workshop on Parallel Graphics and Visualization
    • EGPGV13: Eurographics Symposium on Parallel Graphics and Visualization
    • View Item
    •   Eurographics DL Home
    • Eurographics Workshops and Symposia
    • EGPGV: Eurographics Workshop on Parallel Graphics and Visualization
    • EGPGV13: Eurographics Symposium on Parallel Graphics and Visualization
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting

    Thumbnail
    View/Open
    001-008.pdf (447.4Kb)
    Date
    2013
    Author
    Camp, David
    Krishnan, Hari
    Pugmire, David
    Garth, Christoph
    Johnson, Ian
    Bethel, E. Wes
    Joy, Kenneth I.
    Childs, Hank
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    Although there has been significant research in GPU acceleration, both of parallel simulation codes (i.e., GPGPU) and of single GPU visualization and analysis algorithms, there has been relatively little research devoted to visualization and analysis algorithms on GPU clusters. This oversight is significant: parallel visualization and analysis algorithms have markedly different characteristics - computational load, memory access pattern, communication, idle time, etc. - than the other two categories. In this paper, we explore the benefits of GPU acceleration for particle advection in a parallel, distributed-memory setting. As performance properties can differ dramatically between particle advection use cases, our study operates over a variety of workloads, designed to reveal insights about underlying trends. This work has a three-fold aim: (1) to map a challenging visualization and analysis algorithm - particle advection - to a complex system (a cluster of GPUs), (2) to inform its performance characteristics, and (3) to evaluate the advantages and disadvantages of using the GPU. In our performance study, we identify which factors are and are not relevant for obtaining a speedup when using GPUs. In short, this study informs the following question: if faced with a parallel particle advection problem, should you implement the solution with CPUs, with GPUs, or does it not matter?
    BibTeX
    @inproceedings {EGPGV:EGPGV13:001-008,
    booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
    editor = {Fabio Marton and Kenneth Moreland},
    title = {{GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting}},
    author = {Camp, David and Krishnan, Hari and Pugmire, David and Garth, Christoph and Johnson, Ian and Bethel, E. Wes and Joy, Kenneth I. and Childs, Hank},
    year = {2013},
    publisher = {The Eurographics Association},
    ISSN = {1727-348X},
    ISBN = {978-3-905674-45-3},
    DOI = {10.2312/EGPGV/EGPGV13/001-008}
    }
    URI
    http://dx.doi.org/10.2312/EGPGV/EGPGV13/001-008
    Collections
    • EGPGV13: Eurographics Symposium on Parallel Graphics and Visualization

    Eurographics Association copyright © 2013 - 2021 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2021 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA