EPJ Web Conf.
Volume 251, 202125th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|Number of page(s)||7|
|Published online||23 August 2021|
PandAna: A Python Analysis Framework for Scalable High Performance Computing in High Energy Physics
1 Department of Physics, Colorado State University, Fort Collins, Colorado 80523, USA
2 Department of Physics, Indiana University, Bloomington, Indiana 47405, USA
3 Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
* e-mail: firstname.lastname@example.org
Published online: 23 August 2021
Modern experiments in high energy physics analyze millions of events recorded in particle detectors to select the events of interest and make measurements of physics parameters. These data can often be stored as tabular data in files with detector information and reconstructed quantities. Most current techniques for event selection in these files lack the scalability needed for high performance computing environments. We describe our work to develop a high energy physics analysis framework suitable for high performance computing. This new framework utilizes modern tools for reading files and implicit data parallelism. Framework users analyze tabular data using standard, easy-to-use data analysis techniques in Python while the framework handles the file manipulations and parallelism without the user needing advanced experience in parallel programming. In future versions, we hope to provide a framework that can be utilized on a personal computer or a high performance computing cluster with little change to the user code.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.