Year: 2024
Type: conference-paper
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.
Source: Winter Simulation Conference
Institutions: Argonne National Laboratory, University of Chicago
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, University of Chicago, National Energy Research Scientific Computing Center, National Institutes of Health, Office of Science, National Institute on Drug Abuse, National Cancer Institute, National Institute of Allergy and Infectious Diseases, Argonne National Laboratory
Awards: G102163711, G1055550527, G107546177, G1313983767, G1664757510, G2287611682, G2320811059, G262962599, PIPP Phase I: Robust Epidemic Surveillance and Modeling (RESUME), G3075337988, G3361544315, G393092898, G498139845, G513797252, G5580303508, G5607048534, G6848031779, G6918803902, G9000129752, G9000174660, G9000203216, G969889393, G993149817
OpenAlex ID: W4406612967