Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Maria Acosta Flechas"'
Autor:
Michael Wang, Tingjun Yang, Maria Acosta Flechas, Philip Harris, Benjamin Hawks, Burt Holzman, Kyle Knoepfel, Jeffrey Krupa, Kevin Pedro, Nhan Tran
Publikováno v:
Frontiers in Big Data, Vol 3 (2021)
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volu
Externí odkaz:
https://doaj.org/article/d3f7984ea362452e9070d98c67655b65
Autor:
Nhan Tran, Kevin Pedro, Jeffrey Krupa, Burt Holzman, K. Knoepfel, Benjamin Hawks, Philip Harris, Maria Acosta Flechas, Michael Wang, Tingjun Yang
Publikováno v:
Frontiers in Big Data
Frontiers in Big Data, Vol 3 (2021)
DOE / OSTI
Frontiers in Big Data, Vol 3 (2021)
DOE / OSTI
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d500f262fe4a84293888ebef02687950
http://europepmc.org/articles/PMC7931905
http://europepmc.org/articles/PMC7931905
Autor:
Burt Holzman, Shih-Chieh Hsu, Kelvin Lin, Thomas Klijnsma, Dylan Rankin, Yu Lou, Kevin Pedro, Philip Harris, Javier Duarte, Matthew Trahms, Scott Hauck, Nhan Tran, Maria Acosta Flechas, Jeffrey Krupa, Mia Liu, Ta-Wei Ho
Publikováno v:
H2RC@SC
DOE / OSTI
DOE / OSTI
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86cdd16f91ef5bf97dab2923afd43eb2
http://arxiv.org/abs/2010.08556
http://arxiv.org/abs/2010.08556
Autor:
Edita Kizinevič, Todor Trendafilov Ivanov, Farrukh Aftab Khan, Kenyi Hurtado Anampa, Maria Acosta Flechas, Antonio Pérez-Calero Yzquierdo, James Letts, K Larson, Saqib Haleem, David Mason, Marco Mascheroni, Diego Davila Foyo
Publikováno v:
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019), University Adelaide, Adelaide, Australia, November 04-08, 2019, EDP Sciences, 2020, art. no. 03016, p. [1-8]
EPJ Web of Conferences, Vol 245, p 03016 (2020)
EPJ Web of Conferences, Vol 245, p 03016 (2020)
Efforts in distributed computing of the CMS experiment at the LHC at CERN are now focusing on the functionality required to fulfill the projected needs for the HL-LHC era. Cloud and HPC resources are expected to be dominant relative to resources prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::855cf7f55be5b5692e01b99559f4bf95
https://repository.vu.lt/VU:ELABAPDB126821934&prefLang=en_US
https://repository.vu.lt/VU:ELABAPDB126821934&prefLang=en_US
Autor:
Jeffrey Krupa, Maria Acosta Flechas, Kelvin Lin, Jack Dinsmore, Javier Duarte, Scott Hauck, Nhan Tran, Burt Holzman, Natchanon Suaysom, Mia Liu, Thomas Klijnsma, Kevin Pedro, Philip Harris, Dylan Rankin, Matthew Trahms, Shih-Chieh Hsu
Publikováno v:
Machine Learning: Science and Technology. 2:035005
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83450aa7b31930901db6da559af4eacb
https://doi.org/10.1088/2632-2153/abec21
https://doi.org/10.1088/2632-2153/abec21