Optimizing Scientist Time through In Situ Visualization and Analysis

doi:10.1109/MCG.2018.011461533

Title: Optimizing Scientist Time through In Situ Visualization and Analysis

Abstract

In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is often described in terms of supercomputer size and performance in relation to available storage size.

Authors:

^[1];

^[1]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:: 2018-03-08

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

Contributing Org.:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Univ. of Kaiserslautern (Germany); Kitware Inc., Clifton Park, NY (United States)

OSTI Identifier:: 1463483

Report Number(s):: LA-UR-17-28785
Journal ID: ISSN 0272-1716

Grant/Contract Number:: AC52-06NA25396

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Computer Graphics and Applications

Additional Journal Information:: Journal Volume: 38; Journal Issue: 1; Journal ID: ISSN 0272-1716

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; visualization; in situ; data analysis; lossless compression; feature extraction; scientific computing; data visualization; computational modeling; atmospheric modeling; solar system; supercomputers; data models

Citation Formats


                    Patchett, John, and Ahrens, James. Optimizing Scientist Time through In Situ Visualization and Analysis.  United States: N. p., 2018. 
        Web.  doi:10.1109/MCG.2018.011461533.

Copy to clipboard


                    Patchett, John, & Ahrens, James. Optimizing Scientist Time through In Situ Visualization and Analysis.  United States.  doi:10.1109/MCG.2018.011461533.

Copy to clipboard


                    Patchett, John, and Ahrens, James. Thu .  
        "Optimizing Scientist Time through In Situ Visualization and Analysis".  United States.  doi:10.1109/MCG.2018.011461533.  https://www.osti.gov/servlets/purl/1463483.

Copy to clipboard


                    
@article{osti_1463483,

  title        = {Optimizing Scientist Time through In Situ Visualization and Analysis},

  author       = {Patchett, John and Ahrens, James},

  abstractNote = {In situ processing produces reduced size persistent representations of a simulations state while the simulation is running. The need for in situ visualization and data analysis is often described in terms of supercomputer size and performance in relation to available storage size.},

  doi          = {10.1109/MCG.2018.011461533},

  journal      = {IEEE Computer Graphics and Applications},

  number       = 1,

  volume       = 38,

  place        = {United States},

  year         = {2018},

  month        = {3}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

DOI: 10.1109/MCG.2018.011461533

Other availability

Search WorldCat to find libraries that may hold this journal

Figures / Tables:

Figure 1: An image from the Deep Water Impact Ensemble Data Set showing volume fraction of water with a highly customized color map to show structure as the water moves vertically into upper levels of the atmosphere. Water is a potent greenhouse

All figures and tables (5 total)

Save / Share:

Export Metadata

Save to My Library

Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

Similar Records in DOE PAGES and OSTI.GOV collections:

Scalable Data Management, Analysis, and Visualization (SDAV) Institute

Technical Report Ma, Kwan-Liu

As the scale of computation has exploded, the data produced by these simulations has increased in size, complexity, and richness by orders of magnitude, and this trend will continue. Users of scientific computing systems are faced with the daunting task of managing and analyzing their datasets for knowledge discovery, frequently using antiquated tools more appropriate for the teraflop era. While new techniques and tools are available that address these challenges, often application scientists are not aware of these tools, aren't familiar with the tools' use, or the tools are not installed at the appropriate facilities. SDAV deploys, and assists scientistsmore »« less
DOI: 10.2172/1498620

Full Text Available
In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees, In: SC '14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Conference Landge, Aaditya G. ; Pascucci, Valerio ; Gyulassy, Attila ; ... - SC14: INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS

The ever increasing amount of data generated by scientific simulations coupled with system I/O constraints are fueling a need for in-situ analysis techniques. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a post-process to obtain scientific insights. This paper presents two variants of in-situ feature extraction techniques using segmented merge trees, which encode a wide range of threshold based features. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation thatmore »« less
DOI: 10.1109/SC.2014.88
ParaView Catalyst: Enabling In Situ Data Analysis and Visualization

Journal Article Ayachit, Utkarsh ; Bauer, Andrew ; Geveci, Berk ; ... - ISAV2015: Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization

Computer simulations are growing in sophistication and producing results of ever greater fidelity. This trend has been enabled by advances in numerical methods and increasing computing power. However these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. In situ analysis is recognized as one of the ways to address these challenges. In situ analysis movesmore »« less
DOI: 10.1145/2828612.2828624

Full Text Available
ViSUS: Visualization Streams for Ultimate Scalability

Technical Report Pascucci, V

In this project we developed a suite of progressive visualization algorithms and a data-streaming infrastructure that enable interactive exploration of scientific datasets of unprecedented size. The methodology aims to globally optimize the data flow in a pipeline of processing modules. Each module reads a multi-resolution representation of the input while producing a multi-resolution representation of the output. The use of multi-resolution representations provides the necessary flexibility to trade speed for accuracy in the visualization process. Maximum coherency and minimum delay in the data-flow is achieved by extensive use of progressive algorithms that continuously map local geometric updates of the inputmore »« less
DOI: 10.2172/15014677

Full Text Available
Experiments at Scale with In-Situ Visualization Using ParaView/Catalyst in RAGE

Technical Report Kares, Robert John

In this paper I describe some numerical experiments performed using the ParaView/Catalyst in-situ visualization infrastructure deployed in the Los Alamos RAGE radiation-hydrodynamics code to produce images from a running large scale 3D ICF simulation on the Cielo supercomputer at Los Alamos. The detailed procedures for the creation of the visualizations using ParaView/Catalyst are discussed and several images sequences from the ICF simulation problem produced with the in-situ method are presented. My impressions and conclusions concerning the use of the in-situ visualization method in RAGE are discussed.
DOI: 10.2172/1163258

Full Text Available

Similar Records