Frederick C. Harris - US grants

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

The University of Nevada-Reno aims to add a 10 node A100 Graphics Processing Unit (GPU)-based cluster to their pre-existing Central Processing Unit (CPU) cluster with the expressed aim of creating a more central facility to expand access across campus. The projects aims to meet present and anticipated Artificial Intelligence (AI)/Machine Learning (ML) workloads in various research group on campus. Twenty-first century science and engineering is being transformed by the increasing scales of research computing and data. Despite the increase in the demand for large-scale and data-centric computational resources, it is still a struggle to provide domain scientists with the necessary tools and support at campus-levels. Specifically, decentralized computing practices not only bottleneck research because of lack of scale and support, but also decouple computing from higher performance and deeper storage and networks. Accordingly, shifts in institutional cyberinfrastructure strategies are required, with the following guiding priorities: (1) improving user friendly access; (2) removing perceived barriers in the use of scalable infrastructure; and (3) building multidisciplinary communities for next-generation workflows.

The University of Nevada, Reno (UNR) will add a new A100 GPU-based cluster to its pre-existing CPU cluster to introduce a new set of paradigms for interdisciplinary computing infrastructure and expand access across campus. Each A100 OnDemand node is equipped with 24-core CPUs and A100 GPU accelerators, interconnected with Infiniband switches to provide effective access to science drivers at UNR and externally through Open Science Grid. This cluster has potential to meet UNR’s present and anticipated workloads of various research groups on campus by addressing three main requirements: (1) capability to support dozens to hundreds of concurrent interactive session users through the Multi Instance GPU (MIG) capabilities of the A100 GPUs; (2) support for modern tensor core architectures to facilitate machine learning workflows; and (3) colocation on the UNR research perimeter network and adjacency to high performance storage. This project is a significant step for the under-resourced institution in computing resources and capability. The planned integration with Open Science Grid (OSG) will open a door to on-demand access to millions of hours of heterogeneous compute cycles for researchers on campus.

This project is funded through the collaborative efforts of the Office of Advanced Cyberinfrastructure (OAC) and the Established Program to Stimulate Competitive Research (EPSCoR).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Environmental sciences have enormous potential to provide real-time community hazard information because of advances in cyberinfrastructure (CI) and the Internet of Things (IoT). Real-time systems that observe and monitor hydrology, climate, geology, and ecology have historically been difficult to design, implement, and maintain, with challenges ranging from equipment cost to data management. To make it easier for everyone to access the environmental data they need, this project focuses on “democratizing” portions of an existing regional earthquake and wildfire science network in Nevada by integrating new IoT technologies and cutting-edge cloud-ready CI. Together, these create transformative real-time “crowd-participating” environmental data services, assembled on a new Nevada Weather “edge-to-edge” platform, or “NevWx,” developed as part of the project. These elements become community-centered solutions, in which any individual or organization can easily incorporate new sensors in the NevWx scientific platform. The project’s science application aims to shed new light into temperature patterns in and around mountain communities. This interdisciplinary work contributes new science and engineering knowledge along with new physical resources, which helps move forward the adoption and scalability of IoT in regional research and monitoring networks. Given its emphasis on involving the public and local agencies in science, the project also offers an example for transforming societal engagement with research data networks. By incorporating research results in undergraduate and graduate courses at UNR and in training materials at the Nevada State Climate Office (NSCO), the project also provides substantial and unique educational and workforce development opportunities.<br/><br/>To “democratize” the existing regional science network, this project integrates Low Power Wide Area Network (LPWAN) wireless and IoT-focused streaming data CI to create crowd-participatory environmental hazard data on the new NevWx workflow platform. The software and networking solutions are designed to enable any interested individuals to connect new sensors to the project’s infrastructure, register metadata, and freely access and share the data collected from them. System testing consists of installing temperature sensors and network enhancements in urban and wildland areas in the Lake Tahoe Basin. The main components of this project are: (1) CI research, design & implementation, to facilitate data acquisition from sensors, conduct quality control, and make the data available through a web portal. Specific CI goals are: application of IoT topologies on regional infrastructure; development of containerized microservice-based software architecture to support data collection, storage, processing, and curation; and creation of crowd-participatory data services that incorporate FAIR data principles; (2) Research into how disturbance and development influence temperatures in the Lake Tahoe Basin. This includes measuring urban heat island effects in a mountain community, comparing temperatures in recently burned areas to unburned forest, and tracking freezing levels along a major highway into the Lake Tahoe Basin. Tightly connected with CI research and development, the science applications allow testing the NevWx platform in a range of environments and seasons, while providing real-time information pertinent to public health and safety; and (3) Science stakeholder meetings, which are designed to evaluate their interests and preferences about the proposed sensor data and deployments. Through its modern, flexible and adaptable solutions, available to a wide spectrum of researchers, individuals, and organizations, the project has the potential to advance the current state-of-the-art in CI and environmental sciences, help expand our national cyberinfrastructure on regional networks, and contribute to making significant steps towards “sensing by the public,” with benefits for many science communities and public service agencies.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.