2018
Pulido, Jesus; Livescu, Daniel; Kanov, Kalin; Burns, Randal; Canada, Curtis; Ahrens, James; Hamann, Bernd
Remote Visual Analysis of Large Turbulence Databases at Multiple Scales Journal Article
In: Journal of Parallel and Distributed Computing, 2018, ISBN: 0743-7315, (LA-UR-17-20757).
Abstract | Links | BibTeX | Tags: Computer Science, data reduction, Databases, Distributed Systems, Mathematics and Computing, remote visualization, turbulence, Wavelets
@article{info:lanl-repo/lareport/LA-UR-17-20757,
title = {Remote Visual Analysis of Large Turbulence Databases at Multiple Scales},
author = {Jesus Pulido and Daniel Livescu and Kalin Kanov and Randal Burns and Curtis Canada and James Ahrens and Bernd Hamann},
url = {https://www.sciencedirect.com/science/article/pii/S0743731518303927},
doi = {https://doi.org/10.1016/j.jpdc.2018.05.011},
isbn = {0743-7315},
year = {2018},
date = {2018-01-01},
journal = {Journal of Parallel and Distributed Computing},
abstract = {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. 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. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.},
note = {LA-UR-17-20757},
keywords = {Computer Science, data reduction, Databases, Distributed Systems, Mathematics and Computing, remote visualization, turbulence, Wavelets},
pubstate = {published},
tppubtype = {article}
}
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. 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. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.
: . .
1.
Pulido, Jesus; Livescu, Daniel; Kanov, Kalin; Burns, Randal; Canada, Curtis; Ahrens, James; Hamann, Bernd
Remote Visual Analysis of Large Turbulence Databases at Multiple Scales Journal Article
In: Journal of Parallel and Distributed Computing, 2018, ISBN: 0743-7315, (LA-UR-17-20757).
@article{info:lanl-repo/lareport/LA-UR-17-20757,
title = {Remote Visual Analysis of Large Turbulence Databases at Multiple Scales},
author = {Jesus Pulido and Daniel Livescu and Kalin Kanov and Randal Burns and Curtis Canada and James Ahrens and Bernd Hamann},
url = {https://www.sciencedirect.com/science/article/pii/S0743731518303927},
doi = {https://doi.org/10.1016/j.jpdc.2018.05.011},
isbn = {0743-7315},
year = {2018},
date = {2018-01-01},
journal = {Journal of Parallel and Distributed Computing},
abstract = {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. 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. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.},
note = {LA-UR-17-20757},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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. 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. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.