Studies of GEANT4 performance for different ATLAS detector geometries and code compilation methods

Author:

Marcon Caterina,Elén Einar,Madeira Jessica Rebecca,Morgan Benjamin,Smirnova Oxana,Smith David

Abstract

Full detector simulation is known to consume a large proportion of computing resources available to the LHC experiments, and reducing time consumed by simulation will allow for more profound physics studies. There are many avenues to exploit, and in this work we investigate those that do not require changes in the GEANT4 simulation suite. In this study, several factors affecting the full GEANT4 simulation execution time are investigated. A broad range of configurations has been tested to ensure consistency of physical results. The effect of a single dynamic library GEANT4 build type has been investigated and the impact of different primary particles at different energies has been evaluated using GDML and GeoModel geometries. Some configurations have an impact on the physics results and are, therefore, excluded from further analysis. Usage of the single dynamic library is shown to increase execution time and does not represent a viable option for optimization. Lastly, the static build type is confirmed as the most effective method to reduce the simulation execution time.

Publisher

EDP Sciences

Reference17 articles.

1. ATLAS Collaboration, JINST 3, S08003 (2008)

2. Albrecht J. et al., Computing and Software for Big Science 3 (2019)

3. Calafiura P., Catmore J., Costanzo D., Di Girolamo A., Tech. rep., CERN (2020), CERN-LHCC-2020-015 ; LHCC-G-178

4. Agostinelli S. et al., Nucl. Instrum. Meth. A 506, 250 (2003)

5. Marcon C., Smirnova O., Muralidharan S., EPJ Web Conf. 245 (2020)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3