Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01188 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/epjconf/202533701188 | |
| Published online | 07 October 2025 | |
- H. Ather, S. Berkman, G. Cerati, M.J. Kortelainen, K.H.M. Kwok, S. Lantz, S. Lee, B. Norris, M. Reid, A. Reinsvold Hall et al., Exploring code portability solutions for hep with a particle tracking test code, Frontiers in Big Data 7 (2024). 10.3389/fdata.2024.1485344 [Google Scholar]
- R. Fruhwirth, Application of Kalman filtering to track and vertex fitting, Nucl. Instrum. Meth. A262, 444 (1987). 10.1016/0168-9002(87)90887-4 [Google Scholar]
- T. Gamblin, M. LeGendre, M.R. Collette, G.L. Lee, A. Moody, B.R. de Supinski, S. Futral, The Spack Package Manager: Bringing Order to HPC Software Chaos (Austin, Texas, USA, 2015), Supercomputing 2015 (SC’15), lLNL-CONF-669890, https://github.com/spack/spack [Google Scholar]
- The p2r-spack program, https://github.com/hep-cce/p2r-tests [Google Scholar]
- T.A. Collaboration, The new Fast CAlorimeter Simulation in ATLAS (Tech. Rep. ATL-SOFT-PUB-2018-002, 2018) [Google Scholar]
- M. Atif, Z. Dong, C. Leggett, M. Lin, V. Tsulaia, Porting ATLAS Fast Calorimeter Simulation to GPUs with Performance Portable Programming Models, EPJ Web Conf. 295, 11018 (2024). 10.1051/epjconf/202429511018 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- NVIDIA, P. Vingelmann, F.H. Fitzek, Cuda, release: 10.2.89 (2020), https://developer.nvidia.com/cuda-toolkit [Google Scholar]
- J. Kwack, J. Tramm, C. Bertoni, Y. Ghadar, B. Homerding, E. Rangel, C. Knight, S. Parker, Evaluation of performance portability of applications and mini-apps across amd, intel and nvidia gpus, in 2021 International Workshop on Performance, Portability and Productivity in HPC (P3HPC) (IEEE, 2021), pp. 45–56 [Google Scholar]
- C.R. Trott, D. Lebrun-Grandié, D. Arndt, J. Ciesko, V. Dang, N. Ellingwood, R. Gayatri, E. Harvey, D.S. Hollman, D. Ibanez et al., Kokkos 3: Programming model extensions for the exascale era, IEEE Transactions on Parallel and Distributed Systems 33, 805 (2022). 10.1109/TPDS.2021.3097283 [CrossRef] [Google Scholar]
- H.C. Edwards, C.R. Trott, D. Sunderland, Kokkos: Enabling manycore performance portability through polymorphic memory access patterns, Journal of Parallel and Distributed Computing 74, 3202 (2014), domain-Specific Languages and High-Level Frameworks for High-Performance Computing. https://doi.org/10.1016/j.jpdc.2014.07.003 [Google Scholar]
- A. Matthes, R. Widera, E. Zenker, B. Worpitz, A. Huebl, M. Bussmann, Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library (2017), 1706.10086, https://arxiv. org/abs/1706.10086 [Google Scholar]
- R. Reyes, V. Lomüller, in Parallel Computing: On the Road to Exascale (IOS Press, 2016), pp. 673–682 [Google Scholar]
- S. Bak, C. Bertoni, S. Boehm, R. Budiardja, B.M. Chapman, J. Doerfert, M. Eisenbach, H. Finkel, O. Hernandez, J. Huber et al., Openmp application experiences: Porting to accelerated nodes, Parallel Computing 109, 102856 (2022). [Google Scholar]
- W.C. Lin, S. McIntosh-Smith, T. Deakin, Preliminary report: Initial evaluation of stdpar implementations on amd gpus for hpc, arXiv preprint arXiv:2401.02680 (2024). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

