https://doi.org/10.1051/epjconf/202429513002
Massive Scale Data Analytics at LCLS-II
1 SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA
2 Chan Zuckerberg Imaging Institute, 2682 Middlefield Road, Redwood City, CA 94063, USA
* Corresponding author: jana@slac.stanford.edu
Published online: 6 May 2024
The increasing volumes of data produced at light sources such as the Linac Coherent Light Source (LCLS) enable the direct observation of materials and molecular assemblies at the length and timescales of molecular and atomic motion. This exponential increase in the scale and speed of data production is prohibitive to traditional analysis workflows that rely on scientists tuning parameters during live experiments to adapt data collection and analysis. User facilities will increasingly rely on the automated delivery of actionable information in real time for rapid experiment adaptation which presents a considerable challenge for data acquisition, data processing, data management, and workflow orchestration. In addition, the desire from researchers to accelerate science requires rapid analysis, dynamic integration of experiment and theory, the ability to visualize results in near real-time, and the introduction of ML and AI techniques. We present the LCLS-II Data System architecture which is designed to address these challenges via an adaptable data reduction pipeline (DRP) to reduce data volume on-thefly, online monitoring analysis software for real-time data visualization and experiment feedback, and the ability to scale to computing needs by utilizing local and remote compute resources, such as the ASCR Leadership Class Facilities, to enable quasi-real-time data analysis in minutes. We discuss the overall challenges facing LCLS, our ongoing work to develop a system responsive to these challenges, and our vision for future developments.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
