FCC Analysis Facility at BNL

BNL is developing an analysis facility that will be available to all FCC physicists and computer scientists. This facility will provide access to CPU and disk resources, with significant GPU resources expected to become available in the latter part of 2025.

The system architecture and management model will be based on the ATLAS Tier-3 computing environments. It is being organized and maintained to support the computing needs of FCC users, including capabilities such as:

• Logging into interactive sessions
• Running batch and interactive jobs
• Accessing and storing both shared and private data

As FCC workflows evolve, the facility will be continuously updated to support new requirements.
For any requests or feedback, please contact sabidi@bnl.gov.

Tools and Software Support

The Analysis Facility (AF) supports a wide variety of tools tailored for data analysis. These include:

• CERN software via CVMFS
• Grid middleware
• Rucio clients
• Machine learning packages
• MPI (Message Passing Interface)
• JupyterLab with PyROOT
• XCache with automatic data discovery
• GPU acceleration, and more

For any requests or feedback, please contact sabidi@bnl.gov.

AF Discourse

Need help? Have questions or comments?, Visit our AF Discourse Forum (do not confuse with Discord 👾) for user support, contact, friendly discussion, newsletter and more! We'd love to help you have a smooth experience while working at our analysis facilities! Note that there is a specific "BNL FCC analysis facility" section for questions, but the discourse forum is shared as the FCC AF shares many technical setups with the ATLAS AF.

Privacy Disclaimer

This documentation includes the following:

• User Onboarding: details the process of applying for user accounts at BNL
• Quickstart Guides for SSH logins: walkthroughs for accessing the AF through SSH - Start here
• Quickstart Guides for Jupyter logins: highlights the different aspects of Jupyter and how to use it at AF - or here
• Machine Learning Containers: information and use of ML containers
• Data Storing, Accessing, and Sharing: explains the ways users can use their ATLAS data at AFs
• Data Analysis Tutorials: step-by-step tutorials on using AFs for analyses
• Containers: detailed information on container-based data processing and how to use them at AFs
• Using VSCode: detailed guide on using Visual Studio Code.
• FAQ: answers to frequently asked questions