Geant4 Computing Performance Benchmarking and Monitoring

21st International Conference on Computing in High Energy and Nuclear Physics

物理学;计算机科学

Dotti, Andrea^1 ; Daniel Elvira, V.^2 ; Folger, Gunter^3 ; Genser, Krzysztof^2 ; Jun, Soon Yung^2 ; Kowalkowski, James B.^2 ; Paterno, Marc^2

SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park

CA

94025, United States^1

Fermilab0, P.O. Box 500, Batavia

IL

60510, United States^2

CERN, PH Department, Geneva

CH-1211, Switzerland^3

关键词: Compute-intensive tasks;    Computing performance;    Detector configuration;    Detector simulations;    High energy physics experiments (HEP);    Large-scale computing;    Multi- threaded applications;    Performance evaluations;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/664/6/062021/pdf
DOI  :  10.1088/1742-6596/664/6/062021

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Performance evaluation and analysis of large scale computing applications is essential for optimal use of resources. As detector simulation is one of the most compute intensive tasks and Geant4 is the simulation toolkit most widely used in contemporary high energy physics (HEP) experiments, it is important to monitor Geant4 through its development cycle for changes in computing performance and to identify problems and opportunities for code improvements. All Geant4 development and public releases are being profiled with a set of applications that utilize different input event samples, physics parameters, and detector configurations. Results from multiple benchmarking runs are compared to previous public and development reference releases to monitor CPU and memory usage. Observed changes are evaluated and correlated with code modifications. Besides the full summary of call stack and memory footprint, a detailed call graph analysis is available to Geant4 developers for further analysis. The set of software tools used in the performance evaluation procedure, both in sequential and multi-threaded modes, include FAST, IgProf and Open|Speedshop. The scalability of the CPU time and memory performance in multi-threaded application is evaluated by measuring event throughput and memory gain as a function of the number of threads for selected event samples.