AiiDA中的工作流：设计一个高吞吐量、基于事件的引擎，以实现强大的模块化计算工作流
Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows
摘要：
在过去的二十年里，计算科学领域已经发生了巨大的转变，将高通量计算和大数据分析作为科学发现过程的基本支柱。这就需要开发工具和技术来处理大量数据的生成、存储和处理。在这项工作中，我们深入研究了为AiiDA提供动力的工作流引擎，AiiDA是一种广泛采用的，高度灵活的数据库支持的信息学基础设施，重点是数据的可重复性。我们详细介绍了许多设计选择，这些选择是由几个重要目标决定的：从在单个笔记本电脑上运行扩展到高性能超级计算机的能力，管理运行时间从几分之一秒到几周的作业，并同时扩展到数千个作业，所有这一切都同时最大化鲁棒性。简而言之，AiiDA的目标是成为高通量计算科学的瑞士军刀。除了架构，我们还概述了重要的API设计选择，为工作流作者提供了很大的自由度，同时引导他们编写强大的模块化工作流，最终使他们能够编码他们的科学知识，以造福更广泛的科学界。
英文摘要：
Over the last two decades, the field of computational science has seen a dramatic shift towards incorporating high-throughput computation and big-data analysis as fundamental pillars of the scientific discovery process. This has necessitated the development of tools and techniques to deal with the generation, storage and processing of large amounts of data. In this work we present an in-depth look at the workflow engine powering AiiDA, a widely adopted, highly flexible and database-backed informatics infrastructure with an emphasis on data reproducibility. We detail many of the design choices that were made which were informed by several important goals: the ability to scale from running on individual laptops up to high-performance supercomputers, managing jobs with runtimes spanning from fractions of a second to weeks and scaling up to thousands of jobs concurrently, and all this while maximising robustness. In short, AiiDA aims to be a Swiss army knife for high-throughput computational science. As well as the architecture, we outline important API design choices made to give workflow writers a great deal of liberty whilst guiding them towards writing robust and modular workflows, ultimately enabling them to encode their scientific knowledge to the benefit of the wider scientific community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
