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Updated: Jun 29, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
Published on: October 11, 2016
DISTRIBUTED MODEL EXPLORATION WITH EMEWS
Nicholson Collier1,2, Justin M Wozniak2,3, Arindam Fadikar1,2
1Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA.
Abstract:
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.
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