Speakers
Description
The Deep Underground Neutrino Experiment (DUNE) will produce a very large amount of raw data from its Far Detector (FD) as it turns on at the start of the next decade: roughly 30 PB/yr from the first two FD modules. DUNE’s current processing paradigm would necessitate a large amount of both disk space and compute time to process this raw data up to the point at which high-level event reconstruction can be applied. We propose a new “Prompt Processing” paradigm, in which we leverage a mix of High Performance Computing and heterogeneous (CPU/GPU) infrastructure to perform efficient signal processing of DUNE’s raw readout. This signal processing estimates the amount of ionization charge reaching the detectors’ readout elements at a given point in time, and is thus a necessary first step to processing FD readout. A part of this signal processing is the identification of signal Regions-of-Interest (ROIs) within the estimated signal waveform, which would provide on the order of 10-times data reduction. Thus, running this signal processing promptly (within one hour of a file’s lifetime on disk) and using GPU-accelerated algorithms allows us to treat the raw data as “Write Once, Read Never”. In other words, we can write the raw data to tape almost immediately (and leave it there until planned reprocessing), providing DUNE with a reduction both in compute time and disk utilization. This presentation will describe the overall design of the Prompt Processing workflow, our use of GPU-accelerated algorithms to parallelize our signal processing, and a fully Machine-Learning-based ROI finding algorithm which extends the current state-of-the-art in ROI finding.