https://doi.org/10.1051/epjconf/201921402031
Electromagnetic physics vectorization in the GeantV transport framework
1
CERN,
Meyrin 1211,
Switzerland
2
University of Pittsburgh,
PA 15260,
USA
3
Bhabha Atomic Research Centre,
Mumbai 400085
IN
4
Fermi National Accelerator Lab,
IL 60510,
US
5
Institute of Space Science,
Magurele 077125,
RO
6
Tomsk State University,
Tomsk 634050,
Russia
7
University of Nebraska,
NE 68588,
US
8
Centro de Investigación en Computación,
07738 Gustavo A. Madero,,
Mexico
* e-mail: marilena.bandieramonte@cern.ch
Published online: 17 September 2019
The development of the GeantV Electromagnetic (EM) physics package has evolved following two necessary paths towards code modernization. A first phase required the revision of the main electromagnetic physics models and their implementation. The main objectives were to improve their accuracy, extend them to the new high-energy frontier posed by the Future Circular Collider (FCC) programme and allow a better adaptation to a multi-particle flow. Most of the EM physics models in GeantV have been reviewed from theoretical perspective and rewritten with vector-friendly implementations, being now available in scalar mode in the alpha release. The second phase consists of a thorough investigation on the possibility to vectorise the most CPU-intensive physics code parts, such as final state sampling. We have shown the feasibility of implementing electromagnetic physics models that take advantage of SIMD/SIMT architectures, thus obtaining gains in performance. After this phase, the time has come for the GeantV project to take a step forward towards the final proof of concept. This takes shape through the testing of the full simulation chain (transport + physics + geometry) running in vectorized mode. In this paper we will present the first benchmark results obtained after vectorizing a full set of electromagnetic physics models.
© The Authors, published by EDP Sciences, 2019
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.
