26 citations · 53 across the 7 of their papers we have counts for
7 papers
The integration of heterogeneous resources in the CMS Submission Infrastructure for the LHC Run 3 and beyond
Antonio Perez-Calero Yzquierdo, Marco Mascheroni, Edita Kizinevic +5
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 collaborati…
Adoption of a token-based authentication model for the CMS Submission Infrastructure
Antonio Perez-Calero Yzquierdo, Marco Mascheroni, Edita Kizinevic +6
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,…
Repurposing of the Run 2 CMS High Level Trigger Infrastructure as a Cloud Resource for Offline Computing
Marco Mascheroni, Antonio Perez-Calero Yzquierdo, Edita Kizinevic +8
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 w…
HPC resources for CMS offline computing: An integration and scalability challenge for the Submission Infrastructure
Antonio Perez-Calero Yzquierdo, Marco Mascheroni, Edita Kizinevic +5
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 a…
Collaborative Computing Support for Analysis Facilities Exploiting Software as Infrastructure Techniques
Maria Acosta Flechas, Garhan Attebury, Kenneth Bloom +7
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 ne…
FPGAs-as-a-Service Toolkit (FaaST)
Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris +13
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 a…
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Michael Wang, Tingjun Yang, Maria Acosta Flechas +7
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algori…