The Pittsburgh Supercomputing Center (PSC), a joint effort between Carnegie Mellon University and the University of Pittsburgh, has allotted computing time on its Bridges and Bridges-AI platforms for urgent COVID-19 computational research. These resources are available at no cost to scientists.
PSC is part of the COVID-19 HPC Consortium, which encompasses computing capabilities from some of the most powerful and advanced computers in the world. By contributing to this combined effort, the PSC aims to empower researchers around the world to accelerate understanding of the COVID-19 virus and the development of treatments and vaccines that will help to address infections and limit spread of the virus.
With the nation—and the world—disrupted by the COVID-19 pandemic, we at the Pittsburgh Supercomputing Center (PSC) would like to offer our wishes for safety and health for all. To do our part in protecting the country’s wellbeing, we have been working with a national alliance of high-performance computing resources called the COVID-19 HPC Consortium. As part of this effort, computing time on our Bridges and Bridges-AI platforms is being allotted to urgent COVID-19 computational research. By making these resources available at no cost to scientists, we hope to support the development of new treatments to aid people who have contracted the virus and to limit its spread.
Scientists wishing to obtain computing time on Bridges and other resources in XSEDE, the National Science Foundation cyberinfrastructure in which PSC is a leading member, can find more information at the COVID-19 HPC Consortium.
In related news, PSC is working with collaborators at Weill Cornell Medicine to host the COVID-19 database for the National Genomics Data Center of the Chinese People’s Republic. By providing ready access to this important dataset, they hope to enable researchers to better understand the COVID-19 virus, helping to control the pandemic in the U.S. and around the world. PSC will host the database on its Bridges platform, which is optimized for the kind of Big Data analysis that will be necessary for the task.
