TCNJ Wins NSF Grant to Create a New High-Speed Network to Support Data-intensive Science Research and Education
The National Science Foundation’s Campus Cyberinfrastructure program has awarded TCNJ a $500,000 competitive grant for a collaborative project led by the Division of Information Technology and the School of Science.
This grant will fund strategic enhancements to TCNJ’s network infrastructure to enable and expand the innovative and diverse scientific research occurring at the College. Specifically, the grant will allow TCNJ to implement a new high-speed science network and a friction-free “DMZ” (or “DeMilitarized Zone”), which will allow for faster transmission of data and enhanced network security.
The new high-speed science network will directly connect TCNJ’s three science buildings – Biology, Chemistry, and Physics and Mathematics – to the High Performance Computing cluster in the STEM Building via a new 80 Gigabit per second (Gbps) backbone. It will also include 10 Gbps connections to targeted labs, classrooms, and offices within each of the buildings.
A new Science DMZ that uses the SciPass OpenFlow application will also be implemented. SciPass supports friction-free interconnectivity by automatically allowing identified, “good” data flows to bypass the Science Network firewall in real-time.
These ambitious upgrades will expand TCNJ’s research capacity and efficiency, providing students with greater access to research opportunities in laboratories and classrooms, and contributing to the preparation of a computationally literate workforce. In particular, a new data visualization short course and a new computational science group in the summer MUSE program, or “Mentored Undergraduate Summer Experience,” will expose a greater number of undergraduate students to TCNJ’s scientific computing resources.
The network upgrades will also enable faster data acquisition and propagation thereby enhancing the external collaborations of TCNJ’s many computationally intensive researchers, including both faculty members and undergraduate students.
TCNJ’s network enhancements are driven by five projects in computationally intensive fields including:
- High-resolution microscopy, and
- Undergraduate science education.
Beyond these science drivers, the project will have broader impacts in four key areas:
- Furthering access and inclusion,
- Student training and workforce development,
- Science discovery, and
- Enhancing computer science for all.
The science drivers will be coordinated by an interdisciplinary team of seven faculty members: Joseph Baker (Assistant Professor of Chemistry), Paul Wiita (Professor of Physics), Wendy Clement (Associate Professor of Biology), Nathan Magee (Professor of Physics), Nina Peel (Associate Professor of Biology), Monisha Pulimood (Professor and Chair of Computer Science), and Michael Ochs (Professor of Mathematics and Statistics).
The implementation team leading this project’s network enhancements includes Sharon Blanton (Vice President and Chief Information Officer), Jeffrey Osborn (Dean of the School of Science), Leonard Niebo (Director of Enterprise Infrastructure), Shawn Sivy (HPC System Administrator), and Brad Coburn (Associate Director of Communications Technologies).
More about TCNJ’s High Performance Computing Cluster
“ELSA” (Electronic Laboratory for Science and Analysis) is TCNJ’s heterogeneous High Performance Computing (HPC) cluster named after the famous “Born Free” lioness and our mascot, the lion. ELSA’s compute resources are currently comprised of over 143 servers, providing 1,432 central processing unit (CPU) cores, 44 graphics processing units (GPUs), and approximately one petabyte of network-based storage. The cluster is housed in a dedicated Scientific Computing Center in TCNJ’s newly constructed STEM Building.
Example areas of faculty and undergraduate research and teaching using the ELSA cluster include:
- Applied Mathematics
- Big Data
- Catalytic Chemistry
- Evolution & Phylogeny
- Fluid Dynamics
- Genetics & Bioinformatics
- Machine Learning
- Mathematical Biology
- Natural Language Processing
- Synthetic Biology