Get Started with XSEDE
The National Science Foundation's eXtreme Digital (XD) program is making new infrastructure and next-generation digital services available to researchers and educators to handle the huge volumes of digital information
- Service Providers
Service Providers - entities that make a resource visible and coordinated with the national cyberinfrastructure for benefit to the research community - are central to the function of XSEDE
- College Students
The goals of the student engagement program are to prepare and sustain a larger, more diverse pool of undergraduate and graduate students to be future researchers and educators. Students will be recruited nationally.
- Community Outreach
Increasing diversity is vital to America's future and is a foundation for two of XSEDE's strategic goals: Preparing the current and next generation of scholars, researchers, practitioners, and engineers in the use of advanced digital technologies
The Extreme Science and Engineering Discovery Environment (XSEDE) is a single virtual system that scientists can use to interactively share computing resources, data and expertise. People around the world use these resources and services — things like supercomputers, collections of data and new tools — to improve our planet.
COVID-19 Response: To our valued stakeholders and XSEDE collaborators,
By now you have received a flurry of communication surrounding the ongoing COVID-19 pandemic and how various organizations are responding, and XSEDE is no exception. As XSEDE staff have transitioned out of their usual offices and into telecommuting arrangements with their home institutions, they have worked both to support research around the pandemic and to ensure we operate without interruption.
Computational Research Techniques: Scientific Visualization
June 30, July 7, 14, 21
Computational Research Techniques: Applied Parallel Programming
July 2, 9, 16, 23
Indispensable Security: Tips to Use SDSC's HPC Resources Securely
Thursday, July 16, 2020, at 11:00am PDT
TACC Summer Institute Series: Machine Learning