Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval

Abstract

Extremely high data rates at modern synchrotron and X-ray free-electron lasers (XFELs) light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes. Regardless of the application, the basic concept is the same: data collected in early stages of an experiment, data from past similar experiments, and/or data simulated for the upcoming experiment are used to train machine learning models that, in effect, learn specific characteristics of those data; these models are then used to process subsequent data more efficiently than would general-purpose models that lack knowledge of the specific dataset or data class. Thus, a key challenge is to be able to train models with sufficient rapidity that they can be deployed and used within useful timescales. We describe here how specialized data center AI systems can be used for this purpose.

Fast and accurate learned multiresolution dynamical downscaling for precipitation

“…Once the model is trained, it takes less than a minute on one GPU to downscale 3 months of coarseresolution precipitation data. Both training and inference for downscaling can be further accelerated using purposely built AI systems (Liu et al, 2021;Abeykoon et al, 2019) when dealing with large datasets, e.g., downscaling variables of the Energy Exascale Earth System Model (E3SM Project, DOE, 2018, 2020) for higher spatial-temporal resolution.…”