分布模型与EMEWS的探索
Nicholson Collier1,2, Justin M Wozniak2,3, Arindam Fadikar1,2
1Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, IL, USA.
概括
本教程介绍了用于高性能计算的 Swift (EMEWS) 极大规模模型探索框架的进展. 新功能增强了用于大规模计算模型分析的可访问性和工作流分布.
科学领域:
- 计算科学 计算科学
- 高性能计算 高性能计算
- 软件框架 软件框架
背景情况:
- 高性能计算 (HPC) 资源的可用性不断增加,使新的模拟和计算工具应用成为可能.
- 模型探索 (ME) 框架对于大规模分析至关重要,例如计算模型的校准和优化.
研究的目的:
- 介绍"极大规模模型探索与Swift (EMEWS) "框架的最新进展.
- 突出提高可访问性,分布式工作流程和项目创建的新功能.
主要方法:
- 专注于三个使用灵感的EMEWS功能:二进制安装,与任务API解的架构 (EMEWS DB) 和增强的项目创建.
- 演示EMEWS DB将Python贝叶斯优化算法连接到异质计算资源 (本地和远程).
主要成果:
- 通过二进制安装改进了可访问性.
- 新的脱架构 (EMEWS DB) 和任务API使异质资源上的分布式工作流成为可能.
- 增强创建和管理EMEWS项目的能力.
结论:
- 提出的EMEWS进步有助于对HPC资源进行大规模模型探索.
- 工作示例展示了框架在连接各种计算工具和资源方面的实用性.
- 更多的细节和代码是公开的,促进更广泛的采用和研究.
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