Machine Learning and High-Performance Computing: Theory and Applications

Dear Colleagues,

The rapid growth of data-driven science and engineering has fueled unprecedented advances in machine learning (ML), while the increasing demand for large-scale computation has pushed the limits of high-performance computing (HPC). At their intersection lies a transformative research frontier: leveraging ML to optimize HPC workflows and employing HPC to accelerate the training and deployment of ML models. This convergence is reshaping scientific discovery, engineering design, and decision-making in domains ranging from climate modeling and materials science to healthcare, energy, and autonomous systems.

Studying the integration of ML and HPC is vital for several reasons. First, it enables the efficient processing and analysis of massive datasets, unlocking insights that would otherwise remain inaccessible. Second, it enhances the scalability of ML algorithms, making them applicable to real-world, mission-critical problems. Third, it offers innovative strategies for reducing computational costs and energy consumption through adaptive scheduling, model compression, and hybrid execution frameworks. Finally, it opens opportunities for robust, explainable, and trustworthy ML in safety-critical environments where reproducibility and resilience are essential.

With this in mind, we are pleased to announce this Special Issue, “Machine Learning and High-Performance Computing: Theory and Applications”. It seeks to highlight cutting-edge developments that advance the theoretical understanding, algorithmic innovation, and practical deployment of ML-HPC synergy. We welcome both original research contributions and survey articles that expand the horizons of this interdisciplinary field.

Topics of interest include, but are not limited to, the following:

• Scalable ML algorithms for HPC platforms.
• GPU/accelerator-enabled ML frameworks.
• ML-guided optimization of HPC workflows and energy efficiency.
• Hybrid simulation–ML pipelines for scientific discovery.
• Fault tolerance, reproducibility, and resilience in ML-HPC systems.
• Applications in power systems, computational biology, climate science, autonomous systems, and beyond.
• Benchmarking, datasets, and reproducible research artifacts.

By bringing together diverse perspectives from computer science, engineering, and applied sciences, this Special Issue aims to foster collaboration and accelerate innovation in the co-evolution of ML and HPC. We look forward to receiving your contributions to this exciting and rapidly growing area.

Dr. Shadi G. Alawneh
Guest Editor

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