This project connects the upgraded Advanced Photon Source’s brighter x-ray experiments to supercomputers—processing huge datasets in near real time with shared workflows—to speed breakthroughs across materials, biology, energy, and more.
The Advanced Photon Source (APS), located at Argonne National Laboratory, is a synchrotron light source funded by the U.S Department of Energy (DOE), Office of Science-Basic Energy Sciences (BES) to produce high-energy, high-brightness x-ray beams. As part of the facility’s recent upgrade project, the APS has replaced the storage ring with a new advanced design, and is in the midst of commissioning new and enhanced beamlines offering transformative analytics for x-ray, scattering spectroscopy, and imaging. More than ever before, advanced computational approaches and technologies are essential to fully unlock the scientific potential of the APS.
This project will deliver computational capabilities by leveraging Integrated Research Infrastructure (IRI) to couple APS instruments with supercomputers. Integrating this research infrastructure will be accomplished by running a set of scientific software and workflows using common Globus workflow infrastructure for reduction, reconstruction, multi-modal data utilization, and Artificial Intelligence (AI) training of massive x-ray scattering, spectroscopy, and imaging data in near real-time during experiments, and for post processing and refinement after experiments.
This project will help to enable groundbreaking photon science-enabled research at the upgraded APS, especially for the new and improved beamlines that are enabled by the facility’s brighter, more intense x- rays. It will open a pathway for the critical data processing required for the facility’s user community to conduct cutting-edge basic and applied research in the fields of materials science, biological and life science, physics, chemistry, environmental, geophysical, and planetary science, and innovative x-ray instrumentation. Moreover, this work will enlighten solutions around IRI by informing decisions regarding the management of resource scheduling across facilities, lead to better understanding and optimization tradeoffs between storage, network, and memory bandwidth and latency, and solidify a canonical set of workflows and workflow infrastructure for major classes of light source-enabled research.