349 citations · 4.2k across the 80 of their papers we have counts for
81 papers
Optimizing High Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server
Claire Savard, Nicholas Manganelli, Burt Holzman +5
With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resou…
The U.S. CMS HL-LHC R&D Strategic Plan
Oliver Gutsche, Tulika Bose, Margaret Votava +13
The HL-LHC run is anticipated to start at the end of this decade and will pose a significant challenge for the scale of the HEP software and computing infrastructure. The mission o…
Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing
Tejin Cai, Kenneth Herner, Tingjun Yang +6
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 expe…
Physics Community Needs, Tools, and Resources for Machine Learning
Philip Harris, Erik Katsavounidis, William Patrick McCormack +18
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this w…
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…
Analysis Facilities for HL-LHC
Doug Benjamin, Kenneth Bloom, Brian Bockelman +9
The HL-LHC presents significant challenges for the HEP analysis community. The number of events in each analysis is expected to increase by an order of magnitude and new techniques…