| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 02032 | |
| Number of page(s) | 7 | |
| Section | 2 - Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202024502032 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024502032
CMS Experience with Adoption of the Community supported DD4hep Toolkit
1
Dept. of Physics, University of Wisconsin-Madison, USA
2
Fermi National Accelerator Laboratory, USA
3
CERN, Geneva, Switzerland
4
Tomsk State University, Russia
5
ENEA, Bologna, Italy
6
INFN Sezione di Bologna, Italy
7
Princeton Institute for Computational Science & Engineering, Princeton University, New Jersey, USA
8
The Catholic University of America, Washington D.C., USA
* e-mail: covuosalo@wisc.edu
Published online: 16 November 2020
DD4hep is an open-source software toolkit that provides comprehensive and complete generic detector descriptions for high energy physics (HEP) detectors. The Compact Muon Solenoid collaboration (CMS) has recently evaluated and adopted DD4hep to replace its custom detector description software. CMS has demanding software requirements as a very large, longrunning experiment that must support legacy geometries and study many possible upgraded detector designs of a constantly evolving detector that will be taking data for many years to come. CMS has chosen DD4hep since it is a high-quality, community-supported solution that will benefit from continuing modernization and maintenance. This presentation will discuss the issues of DD4hep adoption, the advantages and disadvantages of the various design choices, performance results, and the integration of the plugin systems from CMS and Gaudi, another open-source software framework. Recommendations about DD4hep based upon the CMS use cases will also be presented.
© The Authors, published by EDP Sciences, 2020
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.
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