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GeantV alpha release

In the fall 2016, GeantV went through a thorough community evaluation of the project status and of its strategy for sharing the R&D; results with the LHC experiments and with the HEP simulation community in general. Following this discussion, GeantV has engaged onto an ambitious 2-year road-path...

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Detalles Bibliográficos
Autores principales: Amadio, G, Ananya, Apostolakis, J, Bandieramonte, M, Behera, S, Bhattacharyya, A, Brun, R, Canal, P, Carminati, F, Cosmo, G, Drogan, V, Duhem, L, Elvira, D, Genser, K, Gheata, A, Gheata, M, Goulas, I, Hariri, F, Ivantchenko, V, Jun, S, Karpinski, P, Khattak, G, Konstantinov, D, Kumawat, H, Lima, G, Martínez-Castro, J, Mendez Lorenzo, P, Miranda-Aguilar, A, Nikolics, K, Novak, M, Orlova, E, Pokorski, W, Ribon, A, Sehgal, R, Schmitz, R, Sharan, S, Shadura, O, Vallecorsa, S, Wenzel, S
Lenguaje:eng
Publicado: 2018
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/1085/3/032037
http://cds.cern.ch/record/2665497
Descripción
Sumario:In the fall 2016, GeantV went through a thorough community evaluation of the project status and of its strategy for sharing the R&D; results with the LHC experiments and with the HEP simulation community in general. Following this discussion, GeantV has engaged onto an ambitious 2-year road-path aiming to deliver a beta version that has most of the final design and several performance features of the final product, partially integrated with some of the experiment’s frameworks. The initial GeantV prototype has been updated to a vector-aware concurrent framework, which is able to deliver high-density floating-point computations for most of the performance-critical components such as propagation in field and physics models. Electromagnetic physics models were adapted for the specific GeantV requirements, aiming for the full demonstration of shower physics performance in the alpha release at the end of 2017. We have revisited and formalized GeantV user interfaces and helper protocols, allowing to: connect to user code, provide recipes to access efficiently MC truth and generate user data in a concurrent environment.