Distributed Model Exploration with EMEWS

As high-performance computing resources have become increasingly available, new modes of applying and experimenting with simulation and other computational tools have become possible. This tutorial presents recent advancements to the Extreme-scale Model Exploration with Swift (EMEWS) framework. EMEWS is a high-performance computing (HPC) model exploration (ME) framework, developed for large-scale analyses (e.g., calibration, optimization) of computational models. We focus on three new use-inspired EMEWS capabilities, improved accessibility through binary installation, a new decoupled architecture (EMEWS DB) and task API for distributing workflows on heterogeneous compute resources, and improved EMEWS project creation capabilities. We present a complete worked example where EMEWS DB is used to connect a Python Bayesian optimization algorithm to worker pools running both locally and on remote compute resources. The example, including an R version, and additional details on EMEWS are made available on a public website.