EPJ Web of Conferences 214, 03004 (2019)
https://doi.org/10.1051/epjconf/201921403004

Producing Madgraph5_aMC@NLO gridpacks and using TensorFlow GPU resources in the CMS HTCondor Global Pool

¹ University of Nebraska-Lincoln, Lincoln, NE, USA
² University of California San Diego, La Jolla, CA, USA
³ Autonomous University of Puebla, Puebla, Mexico
⁴ University of Notre Dame, Notre Dame, IN, USA
⁵ Fermi National Accelerator Laboratory, Batavia, IL, USA
⁶ Port d’Informació Científica, Barcelona, Spain
⁷ Centro de Investigaciones Energéticas Medioambientales y Tecnológicas, Madrid, Spain
⁸ University of Sofia, Sofia, Bulgaria

Published online: 17 September 2019

Abstract

The CMS experiment has an HTCondor Global Pool, composed of more than 200K CPU cores available for Monte Carlo production and the analysis of da.The submission of user jobs to this pool is handled by either CRAB, the standard workflow management tool used by CMS users to submit analysis jobs requiring event processing of large amounts of data, or by CMS Connect, a service focused on final stage condor-like analysis jobs and applications that already have a workflow job manager in place. The latest scenario canbring cases in which workflows need further adjustments in order to efficiently work in a globally distributed pool of resources. For instance, the generation of matrix elements for high energy physics processes via Madgraph5_aMC@NLO and the usage of tools not (yet) fully supported by the CMS software, such as Ten-sorFlow with GPUsupport, are tasks with particular requirements. A special adaption, either at the pool factory level (advertising GPU resources) or at the execute level (e.g: to handle special parameters that describe certain needs for the remote execute nodes during submission) is needed in order to adequately work in the CMS global pool. This contribution describes the challenges and efforts performed towards adaptingsuch workflows so they can properly profit from the Global Pool via CMS Connect.