As part of the ALCF's Service-Enabled Science training series, this session will explore how ALCF supports experimental scientists with time-sensitive computing needs.

Modern scientific experiments increasingly generate large volumes of data that demand high-performance computing, often available only at major facilities. To meet these needs, ALCF provides a suite of tools and capabilities that allow researchers to quickly launch jobs and obtain results.

In this webinar, ALCF's Christine Simpson and Argonne's Hannah Parraga will discuss the use of on-demand queues, service accounts, and Globus tools for enabling these workloads. They will highlight an example case from the Advanced Photon Source, where an automated service for X-ray Photon Correlation Spectroscopy (XPCS) analysis connects beamline scientists to GPUs on ALCF's Polaris system for rapid data analysis.

ALCF's support for experiment-time computing is part of the Department of Energy's Integrated Research Infrastructure program and complements tools and approaches used at other DOE computing facilities.

Christine Simpson is an Assistant Computational Scientist in the Data Science group at ALCF, where she focuses on workflows.  Her background is in computational astrophysics, and she has worked with hydrodynamical simulations to explore questions in cosmology, galaxy evolution, and interstellar medium studies.  Christine received a BA in Mathematics from Wellesley College in 2005 and an MA in Astronomy from Wesleyan University in 2007.  She received her PhD in Astronomy from Columbia University in 2014.  She then held postdoctoral appointments at the Heidelberg Institute for Theoretical Studies and the University of Chicago before joining ALCF in 2022.

Hannah Parraga is a Software Engineer with a focus in data management at Argonne National Laboratory. As a member of the Argonne team, she works on cutting-edge technology that enables us to manage and analyze large volumes of data efficiently and effectively. She is passionate about finding innovative solutions to complex data challenges, and is dedicated to ensuring that our research and development efforts are supported by robust and reliable software systems.