Paper Overview
- Field: Machine Learning
- Author: Jamie J. Alnasir
- Published: 2025-06-11
- arXiv: 2506.08630
- Environment portability via containerisation
- Strategic deployment of job arrays
- Explicit feedback loop mechanisms
- Small-file I/O optimisation
- Managing data gravity and heterogeneous resources
- Reproducibility of AI-driven scientific workflows
Summary
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 mechanisms, and small-file I/O optimisation — the paper offers a framework for transitioning from rigid execution pipelines to adaptive, intelligent computing environments. These architectural principles apply broadly to distributed environments, with particular attention to the resource-intensive throughput demands of modern computational biology.