Designing FAIR Workflows at OLCF: Building Scalable and Reusable Ecosystems for HPC Science

TL;DR

This paper discusses designing FAIR-compliant workflows at HPC centers to enhance sharing, reuse, and collaboration of computational components across disciplines, leveraging infrastructure inspired by EOSC to support diverse research needs.

Contribution

It proposes a model for FAIR workflows tailored to HPC environments, emphasizing reusable workflow components to foster cross-disciplinary collaboration.

Findings

Built on EOSC architecture for HPC

Focus on FAIR individual workflow components

Supports multi-disciplinary research ecosystems

Abstract

High Performance Computing (HPC) centers provide advanced infrastructure that enables scientific research at extreme scale. These centers operate with hardware configurations, software environments, and security requirements that differ substantially from most users' local systems. As a result, users often develop customized digital artifacts that are tightly coupled to a given HPC center. This practice can lead to significant duplication of effort as multiple users independently create similar solutions to common problems. The FAIR Principles offer a framework to address these challenges. Initially designed to improve data stewardship, the FAIR approach has since been extended to encompass software, workflows, models, and infrastructure. By encouraging the use of rich metadata and community standards, FAIR practices aim to make digital artifacts easier to share and reuse, both within and across scientific domains. Many FAIR initiatives have emerged within individual research communities, often aligned by discipline (e.g. bioinformatics, earth sciences). These communities have made progress in adopting FAIR practices, but their domain-specific nature can lead to silos that limit broader collaboration. Thus, we propose that HPC centers play a more active role in fostering FAIR ecosystems that support research across multiple disciplines. This requires designing infrastructure that enables researchers to discover, share, and reuse computational components more effectively. Here, we build on the architecture of the European Open Science Cloud (EOSC) EOSC-Life FAIR Workflows Collaboratory to propose a model tailored to the needs of HPC. Rather than focusing on entire workflows, we emphasize the importance of making individual workflow components FAIR. This component-based approach better supports the diverse and evolving needs of HPC users while maximizing the long-term value of their work.