Status of DUNE Offline Computing

Author:

Kirby Michael

Abstract

We summarize the status of Deep Underground Neutrino Experiment (DUNE) Offline Software and Computing program. We describe plans for the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes in pursuit of the experiment’s physics goals of precision measurements of neutrino oscillation parameters, detection of astrophysical neutrinos, measurement of neutrino interaction properties and searches for physics beyond the Standard Model. In contrast to traditional HEP computational problems, DUNE’s Liquid Argon Time Projection Chamber data consist of simple but very large (many GB) data objects which share many characteristics with astrophysical images. We have successfully reconstructed and simulated data from 4% prototype detector runs at CERN. The data volume from the full DUNE detector, when it starts commissioning late in this decade will present memory management challenges in conventional processing but significant opportunities to use advances in machine learning and pattern recognition as a frontier user of High Performance Computing facilities capable of massively parallel processing. Our goal is to develop infrastructure resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves.

Publisher

EDP Sciences

Reference8 articles.

1. Rucio: Scientific Data Management

2. Wuerthwein Frank, et al., “Data Access in DOMA”, HOW2019 (Joint HSF/OSG/WLCG Workshop). https://indico.cern.ch/event/759388/contributions/3312487/

3. Sfiligoi Igor, Bradley Daniel C., Holzman Burt, Mhashilkar Parag, Padhi Sanjay, and Wurthwein Frank. 2009. “The Pilot Way to Grid Resources Using glideinWMS”. In Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering ­ Volume 02 (CSIE ’09). IEEE Computer Society, USA, 428–432. https://doi.org/10.1109/CSIE.2009.950

4. DUNE Collaboration, DUNE Offline Computing Conceptual Design Report, https://arxiv.org/abs/2210.15665 (2022).

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