Extending the Fermi-LAT Data Processing Pipeline to the Grid

TL;DR

This paper describes the extension of the Fermi-LAT data processing pipeline to incorporate grid computing resources, enhancing automation, monitoring, and resource utilization for large-scale data analysis.

Contribution

It introduces a new interface to Grid systems, enabling efficient use of distributed computing resources for Fermi-LAT data processing.

Findings

Successful integration with Grid resources via Dirac system

Enhanced monitoring and workflow management capabilities

Improved scalability and resource utilization

Abstract

The Data Handling Pipeline ("Pipeline") has been developed for the Fermi Gamma-Ray Space Telescope (Fermi) Large Area Telescope (LAT) which launched in June 2008. Since then it has been in use to completely automate the production of data quality monitoring quantities, reconstruction and routine analysis of all data received from the satellite and to deliver science products to the collaboration and the Fermi Science Support Center. Aside from the reconstruction of raw data from the satellite (Level 1), data reprocessing and various event-level analyses are also reasonably heavy loads on the pipeline and computing resources. These other loads, unlike Level 1, can run continuously for weeks or months at a time. In addition it receives heavy use in performing production Monte Carlo tasks. The software comprises web-services that allow online monitoring and provides charts summarizing work flow aspects and performance information. The server supports communication with several batch systems such as LSF and BQS and recently also Sun Grid Engine and Condor. This is accomplished through dedicated job control services that for Fermi are running at SLAC and the other computing site involved in this large scale framework, the Lyon computing center of IN2P3. While being different in the logic of a task, we evaluate a separate interface to the Dirac system in order to communicate with EGI sites to utilize Grid resources, using dedicated Grid optimized systems rather than developing our own. (abstract abridged)