  We study the performance of a cloud-based GPU-accelerated inference server to
speed up event reconstruction in neutrino data batch jobs. Using detector data
from the ProtoDUNE experiment and employing the standard DUNE grid job
submission tools, we attempt to reprocess the data by running several thousand
concurrent grid jobs, a rate we expect to be typical of current and future
neutrino physics experiments. We process most of the dataset with the GPU
version of our processing algorithm and the remainder with the CPU version for
timing comparisons. We find that a 100-GPU cloud-based server is able to easily
meet the processing demand, and that using the GPU version of the event
processing algorithm is two times faster than processing these data with the
CPU version when comparing to the newest CPUs in our sample. The amount of data
transferred to the inference server during the GPU runs can overwhelm even the
highest-bandwidth network switches, however, unless care is taken to observe
network facility limits or otherwise distribute the jobs to multiple sites. We
discuss the lessons learned from this processing campaign and several avenues
for future improvements.
