Ozik Jonathan, Collier Nicholson T, Wozniak Justin M, Spagnuolo Carmine
Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL 60439, USA.
Dipartimento di Informatica, ISISLab, Università degli Studi di Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano SA, Salerno, ITALY.
Proc Winter Simul Conf. 2016 Dec;2016:206-220. doi: 10.1109/WSC.2016.7822090. Epub 2017 Jan 19.
As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any "black box" application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.
随着高性能计算资源越来越容易获取,新的计算处理和实验模式成为可能。本教程介绍了使用Swift/T的极端规模模型探索(EMEWS)框架,该框架用于将现有的模型探索方法(例如模型校准、元启发式算法、数据同化)和模拟(或任何“黑箱”应用程序代码)能力与Swift/T并行脚本语言相结合,以便在从桌面计算机到学术集群再到全球超级计算机500强级别的各种计算资源上运行科学工作流程。我们将展示多个用例,从简单的基于代理的模型参数扫描开始,到协调分布式模拟集合的复杂自适应参数空间探索工作流程结束。这些用例发布在一个公共存储库中,供有兴趣的各方下载并自行运行。