Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Flechas, Maria Acosta"'
Autor:
Yzquierdo, Antonio Perez-Calero, Mascheroni, Marco, Kizinevic, Edita, Khan, Farrukh Aftab, Kim, Hyunwoo, Flechas, Maria Acosta, Tsipinakis, Nikos, Haleem, Saqib
While the computing landscape supporting LHC experiments is currently dominated by x86 processors at WLCG sites, this configuration will evolve in the coming years. LHC collaborations will be increasingly employing HPC and Cloud facilities to process
Externí odkaz:
http://arxiv.org/abs/2405.14647
Autor:
Yzquierdo, Antonio Perez-Calero, Mascheroni, Marco, Kizinevic, Edita, Khan, Farrukh Aftab, Kim, Hyunwoo, Flechas, Maria Acosta, Tsipinakis, Nikos, Haleem, Saqib, Wurthwein, Frank
The CMS Submission Infrastructure (SI) is the main computing resource provisioning system for CMS workloads. A number of HTCondor pools are employed to manage this infrastructure, which aggregates geographically distributed resources from the WLCG an
Externí odkaz:
http://arxiv.org/abs/2405.14644
Autor:
Mascheroni, Marco, Yzquierdo, Antonio Perez-Calero, Kizinevic, Edita, Khan, Farrukh Aftab, Kim, Hyunwoo, Flechas, Maria Acosta, Tsipinakis, Nikos, Haleem, Saqib, Spiga, Damiele, Wissing, Christoph, Wurthwein, Frank
The former CMS Run 2 High Level Trigger (HLT) farm is one of the largest contributors to CMS compute resources, providing about 25k job slots for offline computing. This CPU farm was initially employed as an opportunistic resource, exploited during i
Externí odkaz:
http://arxiv.org/abs/2405.14639
Autor:
Yzquierdo, Antonio Perez-Calero, Mascheroni, Marco, Kizinevic, Edita, Khan, Farrukh Aftab, Kim, Hyunwoo, Flechas, Maria Acosta, Tsipinakis, Nikos, Haleem, Saqib
The computing resource needs of LHC experiments are expected to continue growing significantly during the Run 3 and into the HL-LHC era. The landscape of available resources will also evolve, as High Performance Computing (HPC) and Cloud resources wi
Externí odkaz:
http://arxiv.org/abs/2405.14631
Autor:
Cai, Tejin, Herner, Kenneth, Yang, Tingjun, Wang, Michael, Flechas, Maria Acosta, Harris, Philip, Holzman, Burt, Pedro, Kevin, Tran, Nhan
Publikováno v:
Comput Softw Big Sci 7, 11 (2023)
We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we a
Externí odkaz:
http://arxiv.org/abs/2301.04633
Autor:
Flechas, Maria Acosta, Attebury, Garhan, Bloom, Kenneth, Bockelman, Brian, Gray, Lindsey, Holzman, Burt, Lundstedt, Carl, Shadura, Oksana, Smith, Nicholas, Thiltges, John
Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of
Externí odkaz:
http://arxiv.org/abs/2203.10161
Autor:
Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E, Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia
Publikováno v:
Front. Big Data 5, 787421 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Externí odkaz:
http://arxiv.org/abs/2110.13041
Autor:
Rankin, Dylan Sheldon, Krupa, Jeffrey, Harris, Philip, Flechas, Maria Acosta, Holzman, Burt, Klijnsma, Thomas, Pedro, Kevin, Tran, Nhan, Hauck, Scott, Hsu, Shih-Chieh, Trahms, Matthew, Lin, Kelvin, Lou, Yu, Ho, Ta-Wei, Duarte, Javier, Liu, Mia
Publikováno v:
2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC), 2020, pp. 38-47
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:
http://arxiv.org/abs/2010.08556
Autor:
Wang, Michael, Yang, Tingjun, Flechas, Maria Acosta, Harris, Philip, Hawks, Benjamin, Holzman, Burt, Knoepfel, Kyle, Krupa, Jeffrey, Pedro, Kevin, Tran, Nhan
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:
http://arxiv.org/abs/2009.04509
Autor:
Krupa, Jeffrey, Lin, Kelvin, Flechas, Maria Acosta, Dinsmore, Jack, Duarte, Javier, Harris, Philip, Hauck, Scott, Holzman, Burt, Hsu, Shih-Chieh, Klijnsma, Thomas, Liu, Mia, Pedro, Kevin, Rankin, Dylan, Suaysom, Natchanon, Trahms, Matt, Tran, Nhan
Publikováno v:
Mach. Learn.: Sci. Technol. 2 (2021) 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:
http://arxiv.org/abs/2007.10359