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Title: Remote visual analysis of large turbulence databases at multiple scales

The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. In this paper, we present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. Finally, the database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.
Authors:
 [1] ;  [2] ;  [3] ;  [3] ;  [2] ;  [2] ;  [4]
  1. Univ. of California, Davis, CA (United States). Dept. of Computer Science; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Johns Hopkins Univ., Baltimore, MD (United States)
  4. Univ. of California, Davis, CA (United States). Dept. of Computer Science
Publication Date:
Report Number(s):
LA-UR-17-20757
Journal ID: ISSN 0743-7315
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Journal of Parallel and Distributed Computing
Additional Journal Information:
Journal Volume: 120; Journal ID: ISSN 0743-7315
Publisher:
Elsevier
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Databases; Wavelets; Data Reduction; Remote Visualization; Distributed Systems; Turbulence
OSTI Identifier:
1457276

Pulido, Jesus, Livescu, Daniel, Kanov, Kalin, Burns, Randal, Canada, Curtis, Ahrens, James, and Hamann, Bernd. Remote visual analysis of large turbulence databases at multiple scales. United States: N. p., Web. doi:10.1016/j.jpdc.2018.05.011.
Pulido, Jesus, Livescu, Daniel, Kanov, Kalin, Burns, Randal, Canada, Curtis, Ahrens, James, & Hamann, Bernd. Remote visual analysis of large turbulence databases at multiple scales. United States. doi:10.1016/j.jpdc.2018.05.011.
Pulido, Jesus, Livescu, Daniel, Kanov, Kalin, Burns, Randal, Canada, Curtis, Ahrens, James, and Hamann, Bernd. 2018. "Remote visual analysis of large turbulence databases at multiple scales". United States. doi:10.1016/j.jpdc.2018.05.011.
@article{osti_1457276,
title = {Remote visual analysis of large turbulence databases at multiple scales},
author = {Pulido, Jesus and Livescu, Daniel and Kanov, Kalin and Burns, Randal and Canada, Curtis and Ahrens, James and Hamann, Bernd},
abstractNote = {The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. In this paper, we present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. Finally, the database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.},
doi = {10.1016/j.jpdc.2018.05.011},
journal = {Journal of Parallel and Distributed Computing},
number = ,
volume = 120,
place = {United States},
year = {2018},
month = {6}
}