Seminar: The ATLAS Experiment at CERN with PanDA

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The ATLAS experiment at CERN uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, it runs around 2 million jobs per day on hundreds of Grid sites and serving thousands of ATLAS users. In 2017 about 1.5 exabytes of data were processed with PanDA. In 2012 BigPanDA project was started with aim to introduce new types of computing resources into ATLAS computing infrastructure, but also to provide PanDA features to different data-intensive applications for projects and experiments outside of ATLAS and High-Energy and Nuclear Physics. In this talk, we will present accomplishments in PanDA development and discuss possible directions for future work.

Date: October 17, 2018
Time: 1pm PT / 4pm ET
Location: 6th floor large Conference Room #689, Information Sciences Institute, Marina del Rey, CA, USA
Join the Seminar Online: https://bluejeans.com/246945817

Pavlo Svirin, Ph.D. (CERN, Switzerland)

Pavlo Svirin received his Ph.D in distributed computing from the National Technical University of Ukraine in 2014. Pavlo’s research was focused on study of resource brokerage for computing resources in distributed systems. After his Ph.D studies, Pavlo worked as a project associate at CERN for ALICE experiment, where he was responsible for integration of new types of resources into ALICE computing environment, in particular - integration of Titan supercomputer. Currently Pavlo is an advanced applications engineer at Brookhaven National Laboratory. His current responsibilities include adaptation of PanDA Workload Management System (WMS) to experiments and projects beyond ATLAS which utilize HPC and Grid resources, as well as participation in development of ATLAS software for distributed computing.

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