264 citations · 1.2k across the 45 of their papers we have counts for
6 papers · 1 filter
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…
Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation
Oz Amram, Kevin Pedro
Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine…
Refining fast simulation using machine learning
Samuel Bein, Patrick Connor, Kevin Pedro +2
At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Optimal Mass Variables for Semivisible Jets
Kevin Pedro, Prasanth Shyamsundar
Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets…
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…