AUTHOR=Wang Michael , Yang Tingjun , Flechas Maria Acosta , Harris Philip , Hawks Benjamin , Holzman Burt , Knoepfel Kyle , Krupa Jeffrey , Pedro Kevin , Tran Nhan TITLE=GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments JOURNAL=Frontiers in Big Data VOLUME=3 YEAR=2021 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2020.604083 DOI=10.3389/fdata.2020.604083 ISSN=2624-909X ABSTRACT=

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services for Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.