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

collaborators

18 papers

physics.acc-ph2026

Incorporating Heightened Scrutiny into a Large HEP Software Project

Philippe Canal, Chris Green, Kyle Knoepfel +5

In 2023, DUNE began re-evaluating the requirements of its data-processing framework, which led to commissioning a new design that would better fit neutrino physics than the existin…

cs.DC2023

Meld: Exploring the Feasibility of a Framework-less Framework

Kyle J. Knoepfel

HEP data-processing frameworks are essential ingredients in getting from raw data to physics results. But they are often tricky to use well, and they present a significant learning…

hep-ex2023★ 1 cited

Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics

Mohammad Atif, Meghna Battacharya, Paolo Calafiura +17

High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapid…

hep-ex2023

Portable Programming Model Exploration for LArTPC Simulation in a Heterogeneous Computing Environment: OpenMP vs. SYCL

Meifeng Lin, Zhihua Dong, Tianle Wang +6

The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming…

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…