2024-05-18 10:26 |
Detailed record - Similar records
|
2024-04-23 10:35 |
Detailed record - Similar records
|
2024-04-22 16:39 |
Detailed record - Similar records
|
2024-01-03 09:34 |
Detailed record - Similar records
|
2023-12-05 17:10 |
Detailed record - Similar records
|
2023-11-16 12:07 |
Detailed record - Similar records
|
2023-10-04 14:37 |
Detailed record - Similar records
|
2023-10-02 15:17 |
[PUBDB-2023-06055]
Contribution to a conference proceedings/Contribution to a book
Mikyška, J. ; de Mulatier, C. ; Paszynski, M. ; et al
Vecpar – A Framework for Portability and Parallelization
2023Computational Science – ICCS 2023 / Mikyška, Jiří (Editor) [https://orcid.org/0000-0001-6017-2259] ; Cham : Springer Nature Switzerland, 2023, Chapter 18 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-031-35994-1=978-3-031-35995-8 ; doi:10.1007/978-3-031-35995-8 Computational Science – ICCS 2023, 23rd International Conference, PraguePrague, Czech Republic, 3 Jul 2023 - 5 Jul 20232023-07-032023-07-05
Cham : Springer Nature Switzerland, Lecture Notes in Computer Science 14073, 253 - 267 (2023) [10.1007/978-3-031-35995-8_18]2023
Complex particle reconstruction software used by High Energy Physics experiments already pushes the edges of computing resources with demanding requirements for speed and memory throughput, but the future experiments pose an even greater challenge. Although many supercomputers have already reached petascale capacities using many-core architectures and accelerators, numerous scientific applications still need to be adapted to make use of these new resources. [...]
Detailed record - Similar records
|
2023-10-02 15:13 |
[PUBDB-2023-06054]
Conference Presentation
Yeo, B. ; Kusiak, K. A. ; Leggett, C. ; et al
CTD2022: traccc - GPU Track reconstruction demonstrator for HEP
2022Connecting the Dots, Princeton, New JerseyPrinceton, New Jersey, USA, 31 May 2022 - 2 Jun 20222022-05-312022-06-02
[10.5281/ZENODO.8119504]
In the future HEP experiments, there will be a significant increase in computing power required for track reconstruction due to the large data size. As track reconstruction is inherently parallelizable, heterogeneous computing with GPU hardware is expected to outperform the conventional CPUs. [...]
External link: Fulltext
Detailed record - Similar records
|
2023-10-02 12:34 |
Detailed record - Similar records
|
|
|