In situ generated probability distribution functions for interactive post hoc visualization and analysis

Yucong Chris Ye; Tyson Neuroth; Franz Sauer; Kwan-Liu Ma; Giulio Borghesi; Aditya Konduri; Hemanth Kolla; Jacqueline Chen

doi:10.1109/LDAV.2016.7874311

In situ generated probability distribution functions for interactive post hoc visualization and analysis

Yucong Chris Ye, Tyson Neuroth, Franz Sauer, Kwan-Liu Ma, Giulio Borghesi, Aditya Konduri, Hemanth Kolla, Jacqueline Chen

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

The growing power and capacity of supercomputers enable scientific simulations at extreme scale, leading to not only more accurate modeling and greater predictive ability but also massive quantities of data to analyze. New approaches to data analysis and visualization are this needed to support interactive exploration through selective data access for gaining insights into terabytes and petabytes of data. In this paper, we present an in situ data processing method for both generating probability distribution functions (PDFs) from field data and reorganizing particle data using a single spatial organization scheme. This coupling between PDFs and particles allows for the interactive post hoc exploration of both data types simultaneously. Scientists can explore trends in large-scale data through the PDFs and subsequently extract desired particle subsets for further analysis. We evaluate the usability of our in situ method using a petascale combustion simulation and demonstrate the increases in task efficiency and accuracy that the resulting workflow provides to scientists.

Original language	English (US)
Title of host publication	IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings
Editors	Kenneth Moreland, Markus Hadwiger, Ross Maciejewski
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	65-74
Number of pages	10
ISBN (Electronic)	9781509056590
DOIs	https://doi.org/10.1109/LDAV.2016.7874311
State	Published - Mar 8 2017
Event	6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016 - Baltimore, United States Duration: Oct 23 2016 → …

Other

Other	6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016
Country	United States
City	Baltimore
Period	10/23/16 → …

Fingerprint

Keywords

G.3 [Mathematics of Computing]: Probability and Statistics-Distribution Functions
H.3.2 [Information Systems]: Information Storage and Retrieval-Information Storage
I.6.6 [Computing Methodologies]: Simulation and Modeling-Simulation Output Analysis
J.2 [Computer Applications]: Physical Sciences and Engineering-Physics

ASJC Scopus subject areas

Computer Science Applications
Modeling and Simulation

Cite this

Ye, Y. C., Neuroth, T., Sauer, F., Ma, K-L., Borghesi, G., Konduri, A., Kolla, H., & Chen, J. (2017). In situ generated probability distribution functions for interactive post hoc visualization and analysis. In K. Moreland, M. Hadwiger, & R. Maciejewski (Eds.), IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings (pp. 65-74). [7874311] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2016.7874311

In situ generated probability distribution functions for interactive post hoc visualization and analysis. / Ye, Yucong Chris; Neuroth, Tyson; Sauer, Franz; Ma, Kwan-Liu; Borghesi, Giulio; Konduri, Aditya; Kolla, Hemanth; Chen, Jacqueline.

IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings. ed. / Kenneth Moreland; Markus Hadwiger; Ross Maciejewski. Institute of Electrical and Electronics Engineers Inc., 2017. p. 65-74 7874311.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ye, YC, Neuroth, T, Sauer, F, Ma, K-L, Borghesi, G, Konduri, A, Kolla, H & Chen, J 2017, In situ generated probability distribution functions for interactive post hoc visualization and analysis. in K Moreland, M Hadwiger & R Maciejewski (eds), IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings., 7874311, Institute of Electrical and Electronics Engineers Inc., pp. 65-74, 6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016, Baltimore, United States, 10/23/16. https://doi.org/10.1109/LDAV.2016.7874311

Ye YC, Neuroth T, Sauer F, Ma K-L, Borghesi G, Konduri A et al. In situ generated probability distribution functions for interactive post hoc visualization and analysis. In Moreland K, Hadwiger M, Maciejewski R, editors, IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 65-74. 7874311 https://doi.org/10.1109/LDAV.2016.7874311

Ye, Yucong Chris ; Neuroth, Tyson ; Sauer, Franz ; Ma, Kwan-Liu ; Borghesi, Giulio ; Konduri, Aditya ; Kolla, Hemanth ; Chen, Jacqueline. / In situ generated probability distribution functions for interactive post hoc visualization and analysis. IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings. editor / Kenneth Moreland ; Markus Hadwiger ; Ross Maciejewski. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 65-74

@inproceedings{5243a5684c1c4a3ba3ecb8bb21cd1406,

title = "In situ generated probability distribution functions for interactive post hoc visualization and analysis",

abstract = "The growing power and capacity of supercomputers enable scientific simulations at extreme scale, leading to not only more accurate modeling and greater predictive ability but also massive quantities of data to analyze. New approaches to data analysis and visualization are this needed to support interactive exploration through selective data access for gaining insights into terabytes and petabytes of data. In this paper, we present an in situ data processing method for both generating probability distribution functions (PDFs) from field data and reorganizing particle data using a single spatial organization scheme. This coupling between PDFs and particles allows for the interactive post hoc exploration of both data types simultaneously. Scientists can explore trends in large-scale data through the PDFs and subsequently extract desired particle subsets for further analysis. We evaluate the usability of our in situ method using a petascale combustion simulation and demonstrate the increases in task efficiency and accuracy that the resulting workflow provides to scientists.",

keywords = "G.3 [Mathematics of Computing]: Probability and Statistics-Distribution Functions, H.3.2 [Information Systems]: Information Storage and Retrieval-Information Storage, I.6.6 [Computing Methodologies]: Simulation and Modeling-Simulation Output Analysis, J.2 [Computer Applications]: Physical Sciences and Engineering-Physics",

author = "Ye, {Yucong Chris} and Tyson Neuroth and Franz Sauer and Kwan-Liu Ma and Giulio Borghesi and Aditya Konduri and Hemanth Kolla and Jacqueline Chen",

year = "2017",

month = mar,

day = "8",

doi = "10.1109/LDAV.2016.7874311",

language = "English (US)",

pages = "65--74",

editor = "Kenneth Moreland and Markus Hadwiger and Ross Maciejewski",

booktitle = "IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016 ; Conference date: 23-10-2016",

}

TY - GEN

T1 - In situ generated probability distribution functions for interactive post hoc visualization and analysis

AU - Ye, Yucong Chris

AU - Neuroth, Tyson

AU - Sauer, Franz

AU - Ma, Kwan-Liu

AU - Borghesi, Giulio

AU - Konduri, Aditya

AU - Kolla, Hemanth

AU - Chen, Jacqueline

PY - 2017/3/8

Y1 - 2017/3/8

N2 - The growing power and capacity of supercomputers enable scientific simulations at extreme scale, leading to not only more accurate modeling and greater predictive ability but also massive quantities of data to analyze. New approaches to data analysis and visualization are this needed to support interactive exploration through selective data access for gaining insights into terabytes and petabytes of data. In this paper, we present an in situ data processing method for both generating probability distribution functions (PDFs) from field data and reorganizing particle data using a single spatial organization scheme. This coupling between PDFs and particles allows for the interactive post hoc exploration of both data types simultaneously. Scientists can explore trends in large-scale data through the PDFs and subsequently extract desired particle subsets for further analysis. We evaluate the usability of our in situ method using a petascale combustion simulation and demonstrate the increases in task efficiency and accuracy that the resulting workflow provides to scientists.

AB - The growing power and capacity of supercomputers enable scientific simulations at extreme scale, leading to not only more accurate modeling and greater predictive ability but also massive quantities of data to analyze. New approaches to data analysis and visualization are this needed to support interactive exploration through selective data access for gaining insights into terabytes and petabytes of data. In this paper, we present an in situ data processing method for both generating probability distribution functions (PDFs) from field data and reorganizing particle data using a single spatial organization scheme. This coupling between PDFs and particles allows for the interactive post hoc exploration of both data types simultaneously. Scientists can explore trends in large-scale data through the PDFs and subsequently extract desired particle subsets for further analysis. We evaluate the usability of our in situ method using a petascale combustion simulation and demonstrate the increases in task efficiency and accuracy that the resulting workflow provides to scientists.

KW - G.3 [Mathematics of Computing]: Probability and Statistics-Distribution Functions

KW - H.3.2 [Information Systems]: Information Storage and Retrieval-Information Storage

KW - I.6.6 [Computing Methodologies]: Simulation and Modeling-Simulation Output Analysis

KW - J.2 [Computer Applications]: Physical Sciences and Engineering-Physics

UR - http://www.scopus.com/inward/record.url?scp=85017207189&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017207189&partnerID=8YFLogxK

U2 - 10.1109/LDAV.2016.7874311

DO - 10.1109/LDAV.2016.7874311

M3 - Conference contribution

AN - SCOPUS:85017207189

SP - 65

EP - 74

BT - IEEE Symposium on Large Data Analysis and Visualization 2016, LDAV 2016 - Proceedings

A2 - Moreland, Kenneth

A2 - Hadwiger, Markus

A2 - Maciejewski, Ross

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2016

Y2 - 23 October 2016

ER -

Access to Document

10.1109/LDAV.2016.7874311