GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments
Name
fdata-03-604083.pdf
Description
Published version
Size
1.44 MB
Format
Adobe PDF
Checksum (MD5)
1a3a2768d66a16d2a00a31ba7240de05
Author(s)
Wang, Michael
Yang, Tingjun
Flechas, Maria Acosta
Harris, Philip
Hawks, Benjamin
Holzman, Burt
Knoepfel, Kyle
Krupa, Jeffrey
Pedro, Kevin
Tran, Nhan
Date Issued
2021
Journal
Frontiers in Big Data
Publisher
Frontiers Media SA
Citation
Wang, Michael, Yang, Tingjun, Flechas, Maria Acosta, Harris, Philip, Hawks, Benjamin et al. 2021. "GPU-Accelerated Machine Learning Inference as a Service for Computing in Neutrino Experiments." Frontiers in Big Data, 3.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/FDATA.2020.604083
