Abstract
Data taking and analysis infrastructures in HEP (High Energy Physics) have evolved during many years to a well known problem domain. In contrast to HEP, third generation synchrotron light sources, existing and upcoming free electron lasers are confronted with an explosion in data rates driven primarily by recent developments in 2D pixel array detectors. The next generation of detectors will produce data in the region upwards of 50 Gbytes per second. At synchrotrons, data was traditionally taken away by users following data taking using portable media. This will clearly not scale at all.We present first experiences of our new architecture and underlying services based on results taken from the resumption of data taking in April 2015. Technology choices were undertaking over a period of twelve month. The work involved a close collaboration between central IT, beamline controls, and beamline support staff. In addition a cooperation was established between DESY IT and IBM to include industrial research and development experience and skills.Our approach integrates HPC technologies for storage systems and protocols. In particular, our solution uses a single file-system instance with a multiple protocol access, while operating within a single namespace.
Highlights
With a circumference of 2.3 km, PETRA III [1] at DESY (German Electron Synchrotron, [2]) is the biggest and most brilliant synchrotron light source in the world
Apart from minor changes in the SMB layer, the biggest improvement has come from parameter tuning at the GPFS layer
Full user authentication is enforced by NFSv4 ACLs so scientists need to have a valid DESY account to access their data
Summary
With a circumference of 2.3 km, PETRA III [1] at DESY (German Electron Synchrotron, [2]) is the biggest and most brilliant synchrotron light source in the world. While the beamtime is active data can be accessed directly from the beamline file system This enables any experiment to run a quick analysis of produced data, for instance to align specific detector settings before producing the main bulk of data. The architecture provided an automated and controlled migration of data from an experiment (beamline) to the core file-system This includes ownership changes, ACL inclusion and the preparation of following steps within the ”lifecycle management” like archiving to tape and data access from remote clients. This approach enables to scale in terms of bandwidth, capacity and IOPS (Input Output Operations per Seconds) while being able to apply different optimization and scaling strategies for the beamline and the core file-system
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