activity
20102026
most citedGPU-accelerated machine learning inference as a service for computing in neutrino experiments

26 citations · 46 across the 13 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.DC2022

Evolution of HEP Processing Frameworks

Christopher D. Jones, Kyle Knoepfel, Paolo Calafiura +2

HEP data-processing software must support the disparate physics needs of many experiments. For both collider and neutrino environments, HEP experiments typically use data-processin…

physics.comp-ph2022★ 7 cited

Portability: A Necessary Approach for Future Scientific Software

Meghna Bhattacharya, Paolo Calafiura, Taylor Childers +16

Today's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The porta…

hep-ex2022★ 1 cited

Data Storage for HEP Experiments in the Era of High-Performance Computing

Amit Bashyal, Peter Van Gemmeren, Saba Sehrish +3

As particle physics experiments push their limits on both the energy and the intensity frontiers, the amount and complexity of the produced data are also expected to increase accor…

hep-ex2022

DUNE Software and High Performance Computing

Bonnie Fleming, Kyle Knoepfel, Meifeng Lin +6

DUNE, like other HEP experiments, faces a challenge related to matching execution patterns of our production simulation and data processing software to the limitations imposed by m…

physics.ins-det2022

Evaluation of Portable Programming Models to Accelerate LArTPC Detector Simulations

Zhihua Dong, Kyle Knoepfel, Meifeng Lin +2

The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE).…