Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at multiple facilities, data sets distributed across multiple information sources, and potentially cloud. Unfortunately, the obvious methods for constructing continuum platforms, orchestrating workflow tasks, and curating datasets over time fail to achieve scientific requirements for performance, energy, security, and reliability. Furthermore, achieving the best use of continuum resources depends upon the efficient composition and execution of workflow tasks, i.e., combinations of numerical solvers, data analytics, and machine learning. Pacific Northwest National Laboratory's LDRD "Cloud, High-Performance Computing (HPC), and Edge for Science and Security" (CHESS) has developed a set of interrelated capabilities for enabling distributed scientific workflows and curating datasets. This report describes the results and successes of CHESS from the perspective of open science.
@article{arxiv.2410.16093,
title = {Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security},
author = {Nathan Tallent and Jan Strube and Luanzheng Guo and Hyungro Lee and Jesun Firoz and Sayan Ghosh and Bo Fang and Oceane Bel and Steven Spurgeon and Sarah Akers and Christina Doty and Erol Cromwell},
journal= {arXiv preprint arXiv:2410.16093},
year = {2024}
}