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. The project results will be disseminated through different venues and forms that are publicized at the project website (http://cse.unl.edu/~yu/research/nsf15_exascale/).
