Duggan Ben, Metzcar John, Macklin Paul
Indiana University Luddy School of Informatics, Computing and Engineering, 107 S Indiana Ave, Bloomington, IN 47405, USA.
GigaByte. 2021 Jun 4;2021:gigabyte22. doi: 10.46471/gigabyte.22. eCollection 2021.
Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches), as well as storing simulation data, requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster with the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here, we describe DAPT and provide an example demonstrating its use.
现代基于主体的模型（ABM）和其他模拟模型需要对许多不同参数进行评估和测试。管理大规模参数扫描（网格搜索）的测试以及存储模拟数据，需要多个可能可定制的步骤，这些步骤可能因模拟而异。此外，如果模拟和处理任务不能在团队成员或计算资源之间共享，参数测试、处理和分析就会变慢。虽然高性能计算（HPC）越来越容易获得，但使用多台计算机和HPC资源通常可以更快地测试模型。为了解决这些问题，我们创建了分布式自动参数测试（DAPT）Python包。通过将参数托管在在线（通常是免费的）“数据库”中，多个用户可以以分布式方式同时运行参数集，从而实现计算能力的众包。将此与灵活的、可编写脚本的工具集相结合，团队可以快速评估模型并评估其潜在假设。在这里，我们描述了DAPT并提供了一个演示其用法的示例。
