Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows

TL;DR

This paper presents the design and implementation of a scalable, robust, and modular workflow engine for AiiDA, facilitating high-throughput and reproducible computational science across diverse computing environments.

Contribution

It introduces a novel event-based, high-throughput workflow engine for AiiDA, emphasizing scalability, robustness, and flexible API design for scientific reproducibility.

Findings

Engine scales from laptops to supercomputers

Supports job runtimes from seconds to weeks

Enables robust, modular, and reproducible workflows

Abstract

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.