Curtis W. Hillegas, Ph.D. - US grants

The volume of data produced by computational modeling and analysis places increasing demands on researchers, IT staff, and IT infrastructure to store, manage, and move data. At the same time, funding agencies are enforcing and increasing requirements for management of data produced through grant funded projects. These pressures come together to put our institutions in a situation where they do not have established funding and best practices for managing this volume of data and, in addition, researchers and IT professionals do not have the data curation expertise to select data and create metadata to ensure the long term preservation and discoverability of important data. There is a strong need to bring these communities together with the library/archive community to consider data lifecycle management and to develop long term funding and data curation strategies that will help institutions to meet the increasing needs.

The objective of this workshop is to bring together researchers, campus Information Technology (IT) leaders, and library/archive specialists to discuss the topic of data lifecycle management specifically as it relates to computational science and engineering research data. This discussion will result in a common understanding of best practices and funding models for selecting, storing, describing, and preserving this digital data. The workshop will also help to cultivate partnerships between these communities to foster continued developments in the preservation and sharing of research data.

The recommendations and practices developed at this workshop will enable the more effective preservation and sharing of the huge quantity and volume of data sets produced by computational scientists and engineers. This workshop will directly impact the intellectual capabilities of our higher education institutions by initiating a sustained dialog into the lifecycle management of our research data.

Faculty and staff who attend the workshop will return to their institutions better prepared to lead the effort to develop and improve research data lifecycle management practices. As these practices are established on campuses across the country, data will become more available to all institutions including those from economically disadvantaged areas. The broader availability of data will benefit research and education, impacting students and researchers.

Proposal #: 12-29573
PI(s): Hillegas, Curtis W.
Carter, Emily A.; Pretorius, Frans; Spitkovsky, Anatoly; Wood, Eric F.
Institution: Princeton University
Title: MRI/Acq.: Shared Parallel High Performance Storage System to Enable Computational Science and Engineering
Project Proposed:
This project, acquiring a High Performance Computing (HPC) storage system, aims to provide storage and I/O bandwidth required to enable advancement of research to new, previously unattainable areas. Specifically, in astrophysics the storage will allow an increase in dimensionality to perform full 3D modeling of shock formation; in civil and environmental engineering, it will enable the analysis of higher resolution water condition data in both time and space; in mechanical and aerospace engineering, the instrument will facilitate rigorous physical modeling of rate constants for biofuel combustion and fundamental understanding of molecular adsorptions; and in physics it will enable the modeling of gravitational waves and compact object mergers considering a broader range of physics. More generally, the system will enable projects across many disciplines in computational science and engineering.
Since data storage and access have become a bottleneck hampering researchers in the TIGRESS systems, the proposed system should contribute to remove the bottleneck. Understanding that data growth will continue, a modular storage system design has been chosen that will allow the system to grow in capacity and performance as the data deluge continuous to mount. Planned is the purchase of a 1.5 PB storage system based on hardware from NetApp, integrated with servers from SGI by Comnetco. The system will run IBMs General Parallel File System (GPFS) and provide 12 GB/s parallel performance across the institution?s TIGRESS HPC systems.
Broader Impacts:
The instrument will facilitate collaboration by making it easy for researchers to share data within the institution; furthermore, it will foster collaboration nationally and internationally through the included web server facility by allowing researchers to broadly share their data. As a research tool available to postdocs, graduate students, and advanced undergraduate students, including many researchers underrepresented minority groups, the instrument will serve as a training platform, teaching data layout, management and performance optimization.

Scientific researchers on university campuses create, analyze, visualize, and share large and diverse datasets from experimental devices like brain scanners, particle colliders, and genome sequencers. However, these "big data" applications place strain on traditional campus networks, due to rapidly increasing volumes of data, the need for either predictably low latency (to adapt experiments in real time) or high throughput (to transfer large data sets between locations), and sophisticated access-control policies (to protect the privacy of human subjects). To enable the next wave of scientific advances, university campuses must find effective ways to meet these challenging demands, at reasonable cost. The emerging technology of Software-Defined Networking (SDN) lowers the barrier to innovation in network management, and can substantially reduce cost through (i) inexpensive commodity network switches, (ii) greater automation of network configuration, and (iii) novel network-management applications that optimize bandwidth usage. Yet, existing innovation in SDN focuses primarily on the needs of commercial cloud providers, rather than the unique requirements of university campuses and scientific researchers. Princeton University is creating a software-defined campus network that can enable the next generation of data-driven scientific research. The initiative brings together big-data science researchers, computer scientists who are experts in SDN, and the campus Office of Information Technology. Princeton is deploying an open-source SDN platform for monitoring and configuring the network, conducting trials of new ways to support big-data applications, and bridging with the larger community, on and off campus, to support the sharing of scientific data, SDN software, and operational experiences.