Real-Time XFEL Data Analysis at SLAC and NERSC: a Trial Run of Nascent Exascale Experimental Data Analysis
Johannes P. Blaschke (1), Aaron S. Brewster (2), Daniel W. Paley (2),, Derek Mendez (2), Asmit Bhowmick (2), Nicholas K. Sauter (2), Wilko Kr\"oger, (3), Murali Shankar (3), Bjoern Enders (1), Deborah Bard (1) ((1) National, Energy Research Scientific Computing Center

TL;DR
This paper demonstrates real-time data analysis for XFEL experiments at SLAC and NERSC, achieving a 10-minute turnaround time that enables immediate experimental decisions, marking a significant milestone in high-throughput molecular imaging.
Contribution
The authors developed an automated, scalable workflow that enables real-time analysis of XFEL data, matching data acquisition rates and significantly reducing turnaround time.
Findings
Achieved 10-minute turnaround from data collection to molecular reconstruction.
Automated data transfer and analysis pipeline enables real-time decision making.
First XFEL experiment to complete data analysis within 10 minutes.
Abstract
X-ray scattering experiments using Free Electron Lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental protocols; ii) data collection rates are growing exponentially, requiring new scalable algorithms. Here we report our experiences analyzing data from two experiments at the Linac Coherent Light Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori XC40 system, using the Superfacility paradigm: our workflow automatically moves raw data between LCLS and NERSC, where it is analyzed using the software package CCTBX. We achieved real time data analysis with a turnaround…
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Taxonomy
TopicsEnzyme Structure and Function · Advanced X-ray Imaging Techniques · Scientific Computing and Data Management
