The objective of this workflow benchmark is to assess the capability of the High-Performance Computing (HPC) system in supporting dynamic workloads that originate from various data stream sources. These workloads will be processed within the compute nodes. The processed data will then be made available to a wide array of consumers, each potentially consuming unique abstractions of the data. We target an ML4NSE (Machine Learning for Neutron Scattering Experiment) application that employs a Temporal Fusion Transformer (TFT) model to both train on and predict the measurement time for a distinct cluster of peaks. This cluster includes a robust nuclear peak along with six weaker satellite peaks, resulting from the magnetic ordering within a single-crystal sample. The primary objective of this code is to enable near real-time decision-making by leveraging the combined powers of Machine Learning (ML) and High-Performance Computing (HPC). This benchmark provides a self-contained, end-to-end evaluation of a coupled compute/data problem.
