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 | |
| 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
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
}
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
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.
ER -