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Title: WarpIV: In situ visualization and analysis of ion accelerator simulations

Journal Article · · IEEE Computer Graphics and Applications
DOI:https://doi.org/10.1109/MCG.2016.62· OSTI ID:1338940
 [1];  [1];  [1];  [2];  [1];  [1];  [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

© 2016 IEEE. The generation of short pulses of ion beams through the interaction of an intense laser with a plasma sheath offers the possibility of compact and cheaper ion sources for many applications - from fast ignition and radiography of dense targets to hadron therapy and injection into conventional accelerators. To enable the efficient analysis of large-scale, high-fidelity particle accelerator simulations using the Warp simulation suite, the authors introduce the Warp In situ Visualization Toolkit (WarpIV). WarpIV integrates state-of-the-art in situ visualization and analysis using VisIt with Warp, supports management and control of complex in situ visualization and analysis workflows, and implements integrated analytics to facilitate query- and feature-based data analytics and efficient large-scale data analysis. WarpIV enables for the first time distributed parallel, in situ visualization of the full simulation data using high-performance compute resources as the data is being generated by Warp. The authors describe the application of WarpIV to study and compare large 2D and 3D ion accelerator simulations, demonstrating significant differences in the acceleration process in 2D and 3D simulations. WarpIV is available to the public via https://bitbucket.org/berkeleylab/warpiv. The Warp In situ Visualization Toolkit (WarpIV) supports large-scale, parallel, in situ visualization and analysis and facilitates query- and feature-based analytics, enabling for the first time high-performance analysis of large-scale, high-fidelity particle accelerator simulations while the data is being generated by the Warp simulation suite. This supplemental material https://extras.computer.org/extra/mcg2016030022s1.pdf provides more details regarding the memory profiling and optimization and the Yee grid recentering optimization results discussed in the main article.

Research Organization:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Organization:
Accelerator & Fusion Research Division, Computational Research Division; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC02-05CH11231; AC52-07NA27344
OSTI ID:
1338940
Alternate ID(s):
OSTI ID: 1379311; OSTI ID: 1502025
Report Number(s):
LBNL-1005718; LLNL-JRNL-677458; ir:1005718
Journal Information:
IEEE Computer Graphics and Applications, Vol. 36, Issue 3; ISSN 0272-1716
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English