Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.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. 
ProtoDUNE
Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing.We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. 
GPU-accelerated inference server
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. 
DUNE grid job submission tools
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. 
CPU version
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. 
GPU version