PLoS Computational Biology· 2026Q1
Twelve quick tips for designing AI-driven HPC workflows
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- Q1SCImago
- 2026year
Short summary
This guide offers twelve practical tips for designing efficient, scalable, and reproducible AI-driven High-Performance Computing (HPC) workflows, addressing challenges like data gravity and resource management introduced by AI's iterative and probabilistic nature.
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Key points
- AI-driven workflows in HPC are iterative, data-driven, and probabilistic, unlike traditional deterministic pipelines.
- Challenges include data gravity, heterogeneous resource management, and complex orchestration.
- Twelve practical tips are offered for designing efficient, scalable, and reproducible AI-driven HPC workflows.
- Specific strategies include containerization, job arrays, feedback loops, and I/O optimization for small files.
AI-generated from the title and abstract; the full text is not read.
Abstract
High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance. However, the pervasive integration of artificial intelligence (AI) and foundation models into scientific research has introduced a fundamentally new computational paradigm. AI-driven workflows are characteristically iterative, data-driven, and probabilistic, introducing unique challenges regarding data gravity, heterogeneous resource management, and complex workflow orchestration. This guide provides twelve practical tips designed to help researchers design efficient, scalable, and reproducible AI-driven HPC workflows. By addressing critical system-level bottlenecks - such as containerisation for environment portability, strategic deployment of job arrays, explicit feedback loop mechanics, and I/O optimisation for small files - this article offers a framework for transitioning from rigid execution pipelines to adaptive, intelligent computational environments. While these architectural principles are broadly applicable across distributed environments, they are particularly tailored to the resource-intensive throughput demands of modern computational biology.
The authors' abstract, as published at the source. PLoS Computational Biology, 2026 · DOI ↗
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Field: Information Systems and Management
Information Systems and ManagementDecision Sciences