Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Esseiva Julien"'
Autor:
Johnson Seth R., Esseiva Julien, Biondo Elliott, Canal Philippe, Demarteau Marcel, Evans Thomas, Jun Soon Yung, Lima Guilherme, Lund Amanda, Romano Paul, Tognini Stefano C.
Publikováno v:
EPJ Web of Conferences, Vol 295, p 11005 (2024)
Celeritas [1] is a new Monte Carlo (MC) detector simulation code designed for computationally intensive applications (specifically, High Lumi- nosity Large Hadron Collider (HL-LHC) simulation) on high-performance heterogeneous architectures. In the p
Externí odkaz:
https://doaj.org/article/2cb43d289cb0464ca0cb16a0d29f35d2
Autor:
Andriotis Nikolaos, Bocci Andrea, Cano Eric, Cappelli Laura, Di Pilato Tony, Ferragina Luca, Hugo Gabrielle, Kortelainen Matti J., Kwok Martin, Olivera Loyola Juan Jose, Pantaleo Felice, Perego Aurora, Redjeb Wahid, Dewing Mark, Esseiva Julien
Publikováno v:
EPJ Web of Conferences, Vol 295, p 11008 (2024)
In the past years the landscape of tools for expressing parallel algorithms in a portable way across various compute accelerators has continued to evolve significantly. There are many technologies on the market that provide portability between CPU, G
Externí odkaz:
https://doaj.org/article/82e21d8ba1d540f39812cb33b1230401
Autor:
Calafiura Paolo, Esseiva Julien, Ju Xiangyang, Leggett Charles, Stanislaus Beojan, Tsulaia Vakho
Publikováno v:
EPJ Web of Conferences, Vol 295, p 03041 (2024)
With the increased data volumes expected to be delivered by the HLLHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud compu
Externí odkaz:
https://doaj.org/article/744712c24df548d1b9e2162e5d6cdf99
Autor:
Esseiva, Julien, Stanislaus, Beojan, Calafiura, Paolo, Leggett, Charles, Tsulaia, Vakhtang, Ju, Xiangyang
With the increased data volumes expected to be delivered by the HL-LHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________65::dfa5ba944ee98791aa3d249945f57a12
http://cds.cern.ch/record/2857281
http://cds.cern.ch/record/2857281
Autor:
Stanislaus, Beojan, Calafiura, Paolo, Esseiva, Julien, Ju, Xiangyang, Leggett, Charles, Tsulaia, Vakhtang
Description Experiments at the CERN High-Luminosity Large Hadron Collider (HL-LHC) will produce hundreds of Petabytes of data per year. Efficient processing of this dataset represents a significant human resource and technical challenge. Today, ATLAS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________65::b4696829fcefbce2cb8364b022cbeb9f
http://cds.cern.ch/record/2838149
http://cds.cern.ch/record/2838149