Speaker
Xiangyang Ju
(Lawrence Berkeley National Lab. (US))
Description
Machine Learning (ML)-based algorithms play increasingly important roles in almost all aspects of data processing in the ATLAS experiment at CERN. Diverse ML models are used in detector simulation, event reconstruction, and data analysis. They are being deployed in the ATLAS software framework, Athena. Our primary approach to perform ML inference in Athena is to use ONNXRuntime. ONNXRuntime is a cross-platform ML model acceleration library, with a flexible interface to integrate hardware-specific libraries. In this talk, we will describe the ONNXRuntime interface in Athena and the impact of advanced ONNXRuntime settings on various ML models and workflows at ATLAS.
Authors
Attila Krasznahorkay
(CERN)
Beojan Stanislaus
(Lawrence Berkeley National Lab. (US))
Dr
Charles Leggett
(Lawrence Berkeley National Lab (US))
Mr
Debottam Bakshi Gupta
(University of Texas at Arlington (US))
Johannes Elmsheuser
(Brookhaven National Laboratory (US))
Julien Esseiva
(Lawrence Berkeley National Lab. (US))
Paolo Calafiura
(Lawrence Berkeley National Lab. (US))
Vakho Tsulaia
(Lawrence Berkeley National Lab. (US))
Xiangyang Ju
(Lawrence Berkeley National Lab. (US))
