  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.
