By leveraging advanced parallel computing systems, scientists can answer important questions that are critical to US energy and economic security. Exascale computing will further enable scientists to perform detailed simulations at higher resolution and greater complexity. Advanced visualization is necessary for scientists to explore massive and complex simulation data at high interactivity and fidelity to study various physical, chemical, and biological phenomena. Although visualization technology has significantly progressed in recent years, conventional visualization techniques are not yet ready for exascale systems and applications. Future exascale systems are expected to be characterized with many-core processors, deep memory hierarchies, and high levels of concurrency. The design of new visualization techniques must adapt to the need for timely discovery from complex and extremely large data sets as well as these emerging hardware and software trends. The goal of this project is to address the current technology gap by investigating a complete course of visualization pipeline with scientific simulations in a holistic fashion, and thus ensure parallelism and efficiency in exascale data visual analytics. This project will integrate research with teaching and outreach programs, where visualization of scientific applications will be used as an effective means to promote students' interest and proficiency in science and engineering studies, and to attract and retain both undergraduate and graduate students, particularly female students, into research.

This project plans to account directly for the complex interdependencies with and among the critical components of visual analytics for exascale computing. This project focuses on three key research tasks: (1) developing a novel in-situ data reduction and indexing algorithm to capture essentials from large-scale simulations; (2) studying parallel visualization algorithms to promise scalable performance for high-throughput and high-resolution exploration of large-scale simulation data based on in-situ compact data representations; and (3) designing user interface to parse and deploy application knowledge for visual analytics to acquire critical scientific discovery from in-situ simulation output with enhanced user experience and performance. This project is driven by real-world large-scale scientific applications that involve the modeling and analysis of evolving phenomena with heterogeneous data types, and demand scalable capabilities of visual analytics. Scientific collaborators will be involved into the development, evaluation, and deployment of the solutions to close the gap between advanced visualization techniques and scientific applications, and help solve some of the most challenging scientific problems. The techniques developed within this project will be readily adapted for use by many applications beyond the primary demonstration targets with similar needs, and thus will have a significant impact on scientists' capability for data analysis and visualization. The success of this research will potentially change the conventional scientific discovery pipeline and accelerate the study of large-scale simulation data.

Jacqueline Chen	Sandia National Laboratories
Jun Wang	University of Iowa
Kwo-Sen Kuo	University of Maryland, College Park
Haishun Yang	University of Nebraska-Lincoln

Shruti Daggumati	Graduate Student
Xin Liu	Graduate Student
Yu Pan	Graduate Student
Saeideh Samani	Graduate Student
Jin Wang	Graduate Student
Jieting Wu	Graduate Student
Lina Yu	Graduate Student
Jianping Zeng	Graduate Student
Li Zhang	Graduate Student
Feiyu Zhu	Graduate Student
David Cao	Undergraduate Student
Ceren Kaplan	Undergraduate Student
Alec Schneider	Undergraduate Student
Artem Shukaev	Undergraduate Student
Igor Soares	Undergraduate Student

	Uncertainty Analysis and Visualization for Nitrogen Leaching with the Maize-N Model Babak Samani, Saeideh Samani, Haishun Yang, Hongfeng Yu. Proceedings of 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, December 10-13, 2020. DOI: 10.1109/BigData50022.2020.9378105 [Paper]
	View-Dependent Data Prefetching for Interactive Visualization of Large-Scale 3D Scientific Data Jin Wang. Master Thesis, University of Nebraska-Lincoln, December, 2019.
	Exploring Nitrogen Leaching using Uncertainty Visualization Saeideh Samani. Master Thesis, University of Nebraska-Lincoln, December, 2019.
	Adaptive Deep Learning based Time-Varying Volume Compression Yu Pan, Feiyu Zhu, Tian Gao, Hongfeng Yu. Proceedings of 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, December 9-12, 2019. DOI: 10.1109/BigData47090.2019.9006146 [PAPER]
	Improving Short Job Latency Performance in Hybrid Job Schedulers with Dice Wei Zhou, K. Preston White, Hongfeng Yu. Proceedings of the 48th International Conference on Parallel Processing (ICPP), Kyoto, Japan, August 5-8, 2019. DOI: 10.1145/3337821.3337851 [PAPER]
	A Scientific Data Representation Through Particle Flow Based Linear Interpolation Yu Pan, Feiyu Zhu, Hongfeng Yu. Proceedings of 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), San Francisco, CA, April 4-9, 2019. DOI: 10.1109/BigDataService.2019.00010 [PAPER]
	Deploying, Improving And Evaluating Edge Bundling Methods For Visualizing Large Graphs Jieting Wu. Ph.D. Dissertation, University of Nebraska-Lincoln, December, 2018.
	An Information-Theoretic Framework for Evaluating Edge Bundling Visualization Jieting Wu, Feiyu Zhu, Xin Liu, Hongfeng Yu. Entropy, 20(9), 625, 2018. DOI: 10.3390/e20090625 [PAPER]
	A Distributed Infomap Algorithm for Scalable and High-Quality Community Detection Jianping Zeng, Hongfeng Yu. Proceedings of the 47th International Conference on Parallel Processing (ICPP), Eugene, OR, USA, August 13-16, 2018. DOI:10.1145/3225058.3225137 [PAPER]
	Feature Extraction and Parallel Visualization for Large-Scale Scientific Data Lina Yu. Ph.D. Dissertation, University of Nebraska-Lincoln, December, 2017.
	Scalable Parallel Community Detection for Large-Scale Graphs Jianping Zeng. Ph.D. Dissertation, University of Nebraska-Lincoln, December, 2017.
	Visual Analytics with Unparalleled Variety Scaling for Big Earth Data Lina Yu, Michael L. Rilee, Yu Pan, Feiyu Zhu, Kwo-Sen Kuo, Hongfeng Yu. Proceedings of 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, December, 2017, pp. 514-521. [PAPER]
	Boundary-Structure-Aware Transfer Functions for Volume Classification Lina Yu, Hongfeng Yu. Proceedings of SIGGRAPH Asia 2017 Symposium on Visualization (SA), Bangkok, Thailand, November, 2017. DOI: https://doi.org/10.1145/3139295.3139306 [PAPER]
	MLSEB: Edge Bundling Using Moving Least Squares Approximation Jieting Wu, Jianping Zeng, Feiyu Zhu, Hongfeng Yu. Proceedings of 25th International Symposium on Graph Drawing & Network Visualization (GD), Boston, MA, September, 2017. [PAPER]
	An Application-Aware Data Replacement Policy for Interactive Large-Scale Scientific Visualization Lina Yu, Hongfeng Yu, Hong Jiang, Jun Wang. Proceedings of 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lake Buena Vista, FL, 2017, pp. 1216-1225. DOI: 10.1109/IPDPSW.2017.16 [PAPER]
	Legion-based Scientific Data Analytics on Heterogeneous Processors Lina Yu, Hongfeng Yu. Proceedings of 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, December, 2016, pp. 2305-2314. DOI: 10.1109/BigData.2016.7840863 [PAPER]
	A Geohydrologic Data Visualization Framework with an Extendable User Interface Design Yanfu Zhou, Jieting Wu, Lina Yu, Hongfeng Yu, Zhenghong Tang. Proceedings of 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, December, 2016, pp. 2322-2331. DOI: 10.1109/BigData.2016.7840865 [PAPER]
	In-staging Data Placement for Asynchronous Coupling of Task-based Scientific Workflows (Best Paper Award) Qian Sun, Melissa Romanus, Tong Jin, Hongfeng Yu, Peer-Timo Bremer, Steve Petruzza, Scott Klasky, Manish Parashar. Proceedings of The Second International Workshop on Extreme Scale Programming Models and Middleware (ESPM2), held in conjunction with SC16: The International Conference on High Performance Computing, Networking, Storage and Analysis, Salt Lake City, Utah, November, 2016. DOI:10.1109/ESPM2.2016.12 [PAPER]
	A Study of Graph Partitioning Schemes for Parallel Graph Community Detection Jianping Zeng, Hongfeng Yu. Parallel Computing, vol.58, pp.131-139, October, 2016 DOI:10.1016/j.parco.2016.05.008 [PAPER]
	A Study of Scientific Visualization on Heterogeneous Processors Using Legion (Peer Reviewed Poster Paper) Lina Yu, Hongfeng Yu. Proceedings of 2016 IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), held in conjunction with IEEE VisWeek'16, October, 2016. [VIDEO]
	Tweether: A Visualization Tool Displaying Correlation of Weather to Tweets Shruti Daggumati, Igor Soares, Jieting Wu, David Cao, Hongfeng Yu, Jun Wang. Proceedings of IS&T Conference on Visualization and Data Analysis (VDA), February, 2016. DOI:10.2352/ISSN.2470-1173.2016.1.VDA-497 [PAPER]
	Scalable Parallel Distance Field Construction for Large-Scale Applications Hongfeng Yu, Jinrong Xie, Kwan-Liu Ma, Hemanth Kolla, Jacqueline H. Chen. IEEE Transactions on Visualization and Computer Graphics (TVCG), vol.21, no.10, pp.1187-1200, October 1 2015. DOI:10.1109/TVCG.2015.2417572 [PAPER]
	Texture-Based Edge Bundling: A Web-Based Approach for Interactively Visualizing Large Graphs Jieting Wu, Lina Yu, Hongfeng Yu. Proceedings of 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015, pp. 2501-2508. DOI: 10.1109/BigData.2015.7364046 [PAPER]
	Texture-Based Edge Bundling for Graph Visualization Jieting Wu, Hongfeng Yu. IEEE VIS 2015 Posters, October, 2015.
	Parallel Modularity-based Community Detection on Large-scale Graphs (Best Paper Finalist) Jianping Zeng, Hongfeng Yu. Proceedings of 2015 IEEE International Conference on Cluster Computing, Chicago, IL, 2015, pp. 1-10. DOI:10.1109/CLUSTER.2015.11 [PAPER]
	Visual Analytics for Large Communication Trace Data Jieting Wu. Master Thesis, University of Nebraska-Lincoln, May, 2015.

Adaptive Volume Compression
Particle Flow
Boundary-Structure-Aware Transfer Function
Application-Aware Prefetching
CommGram

Our Visualization Demonstration at UNL Annual "Introduce a Girl to Engineering Day", January, 2020
Yu's Paper Presentation at IEEE BIGDATA, December , 2019		Feiyu is preparing Nebraska State FFA Convention Tours, April, 2019
Our Interactive Scientific Visualization Booth at UNL & LPS Annual Hour of Code Event, December, 2018
Jianping's Paper Presentation at the 47th International Conference on Parallel Processing (ICPP), August, 2018		Hongfeng Attended Dagstuhl Seminar on In Situ Visualization for Computational Science, July, 2018
Our Interactive Demonstration at American Geophysical Union (AGU) Fall Meeting, December, 2017
Feiyu's Poster Presentation at AGU Fall Meeting, December, 2017		Yu's Oral Presentations at AGU Fall Meeting, December, 2016
ASH Workshop, held in conjunction with IEEE BigData, December, 2017		Lina's Paper Presentations at IEEE BigData, December, 2017
Lina's Paper Presentation at SIGGRAPH Asia Symposium on Visualization, November, 2017		Jieting's Paper Presentation at Graph Drawing & Network Visualization (GD), September, 2017

Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), held in conjunction with IEEE BigData, December, 2016

International Workshop on Ultrascale Visualization (Ultravis), held in conjunction with ACM/IEEE SC, November, 2015

Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), held in conjunction with IEEE BigData, October, 2015

Hongfeng Yu
Visualization of Big Data in Atmospheric Sciences (Invited Talk)
Atmospheric Chemistry Workshop, University of Nebraska-Lincoln, October, 2015.

Hongfeng Yu
Served as a mentor at IEEE Cluster 2015 NSF Sponsored Student Mentoring Program
IEEE Cluster 2015, Chicago, IL, September, 2015.

01/25/2020	We demonstrated visualization to Nebraska girls in grades 3-12 at the third annual Introduce a Girl to Engineering day. The event was supported by multiple sponsors, including Lincoln Children's Museum, UNL College of Engineering, and so on.
12/02/2019	Saeideh Samani successfully defended her Master thesis.
11/18/2019	Jin Wang successfully defended her Master thesis.
04/03/2019	Our lab participated in Nebraska State FFA Convention Tours for visualization demos and presentations to future farmers of America.
12/08/2018	We had an interactive scientific visualization booth at Hour of Code 2018, an annual event co-hosted by University of Nebraska-Lincoln (UNL) and Lincoln Public Schools (LPS) for promoting interdisciplinary computer technology. There were 980 people registered for this event, including 459 adults and 521 students (most k-12 students). 1011 Now. "Hour of Code event draws hundreds" Lincoln Journal Star. Annual Hour of Code event hosts more than 800 at Innovation Campus The Daily Nebraskan. UNL, LPS to co-host Hour of Code event, promote computing in all disciplines
11/27/2018	Jieting successfully defended his Ph.D. dissertation.
07/1-6/2018	Hongfeng Attended Dagstuhl Seminar on In Situ Visualization for Computational Science, July, 2018.
11/21/2017	Jieting passed the comprehensive exam for his Ph.D. candidacy.
11/13/2017	Jianping successfully defended his Ph.D. dissertation.
09/14/2017	Lina successfully defended her Ph.D. dissertation.
12/13/2016	Lina won a Graduate Student Conference Travel Grant from the College of Engineering at the University of Nebraska-Lincoln.
11/18/2016	Our paper "In-staging Data Placement for Asynchronous Coupling of Task-based Scientific Workflows" was selected as the Best Paper at ESPM2 2016.
11/04/2016	American Scientist. "A Computed Flame: To understand how fuel burns in a diesel engine takes chemistry knowledge and supercomputing muscle."
09/09/2016	Our paper "Parallel Modularity-based Community Detection on Large-scale Graphs" was selected as one of four Best Paper Candidates at IEEE Cluster 2015.
09/02/2016	Science Node. "Climate change hurting your feelings?"
05/11/2016	UNL Today. "Fires a growing threat to air quality, UNL scientist says."
07/08/2015	Jianping won a NSF Travel Award.
06/11/2015	Jianping won a Graduate Student Conference Travel Grant from the College of Engineering at the University of Nebraska-Lincoln.
04/14/2015	Jieting successfully defended his master thesis.
04/10/2015	Shruti successfully defended her master project.