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Issue No. 03 - May-June (2016 vol. 36)
ISSN: 0272-1716
pp: 48-58
Kenneth Moreland , Sandia National Laboratories
Christopher Sewell , Los Alamos National Laboratory
William Usher , University of Utah
Li-ta Lo , Los Alamos National Laboratory
Jeremy Meredith , Oak Ridge National Laboratory
David Pugmire , Oak Ridge National Laboratory
James Kress , University of Oregon
Kwan-Liu Ma , University of California, Davis
Hank Childs , University of Oregon
Matthew Larsen , Lawrence Livermore National Laboratory
Chun-Ming Chen , Ohio State University
Robert Maynard , Kitware
Berk Geveci , Kitware
One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.
Data visualization, Algorithm design and analysis, Software engineering, Message systems, Parallel processing, Computer architecture, Computational modeling

K. Moreland et al., "VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures," in IEEE Computer Graphics and Applications, vol. 36, no. 3, pp. 48-58, 2016.
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