Title: AI on HPC: Performance Engineering, Challenges and Opportunities
Date: June 26, 2026, 9 a.m. – 1 p.m. (to be confirmed)
Co-happening with: ISC High Performance 2026
Type: Workshop

Call for Papers

How can AI workloads be engineered for optimal performance in modern HPC environments?

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has positioned High-Performance Computing (HPC) systems as indispensable platforms for developing, training, and executing these workloads. However, the architectural complexity and batch-oriented design of traditional HPC systems pose unique challenges distinct from those encountered in resource-elastic environments such as clouds.

The parallelization characteristics, input/output requirements, and dynamic workflows of AI workloads demand innovative techniques for efficient utilization of HPC resources. Moreover, the performance engineering of such workloads is crucial to achieve scalability, portability, and reproducibility across diverse system architectures.

This workshop aims to bring together researchers, practitioners, and system developers to discuss engineering challenges, performance optimization, and emerging opportunities at the intersection of AI and HPC. It invites among others, papers that present experimental results, architectural insights, performance studies, and best practices advancing the convergence of these domains.

Important dates

• Submission deadline: March 2, 2026 AOE
• Notification of acceptance: April 13, 2026
• Camera-ready deadline: May 11, 2026 AOE
• Workshop day: June 26, 2026

Submissions

Scientific contribution can be submitted via the ISC Submission portal (not open yet). Submissions should be in LNCS format and contain 6 to 12 pages. Proposals for Lightning talks with a maximum 1 page abstract are highly welcome. Please note that talks are excluded from publication.

Topics of interest, including but are not limited to:

• Characterizing AI/ML workloads on HPC systems
• Data preparation for AI/ML workload on HPC
• Best practices for integrating ML/AI into existing HPC environments
• Efficient inference of LLMs on HPC
• Parallelization strategies for AI/ML
• Performance optimization of AI/ML frameworks on HPC
• AI factories and end-to-end pipelines for scalable AI development on HPC
• Cross-platform portability and reproducibility in AI performance studies
• AI-enhanced HPC simulations for scientific and industrial applications
• Resource allocation and scheduling for AI/ML workloads
• Energy efficiency and power management for AI/ML on HPC
• Hybrid workloads on HPC systems
• HPC-AI/ML convergence for scientific applications
• Next-generation HPC systems for AI/ML
• Industrial AI/ML on HPC
• Collaborative and interactive AI/ML on HPC
• Specialized AI/ML frameworks for HPC
• HPC-AI/ML benchmarking and evaluation
• DevOps and MLOps for HPC-AI/ML

General Chair

• Dr. Siavash Ghiasvand, Dresden University of Technology, ScaDS.AI Dresden/Leipzig, Germany
• Dr. Vijeta Sharma, Norwegian University of Science and Technology (NTNU), Norway
• Dr. Ajeet Ram Pathak, Norwegian University of Science and Technology (NTNU), Norway

Program Chairs:

• Dr. Paramita Mirza (Fraunhofer IIS, Germany)
• Dr. Taras Lazariv (ScaDS.AI Dresden/Leipzig, Germany)
• Dr. Thor Wikfeldt (RISE, Sweden)
• Dr. Yonglei Wang (Linköping University, Sweden)

Program Committee

• Prof. Victor Calo (Curtin University, Australia)
• Prof. Florina M. Ciorba (University Basel, Switzerland)
• Dr. Jens Domke (RIKEN, Japan)
• Dr. Christian Engelmann (Oakridge National Laboratory, USA)
• Robert Henschel (Indiana University, USA)
• Dr. Rene Jäkel (Dresden University of Technology, ScaDS.AI Dresden/Leipzig, Germany)
• Neringa Jurenaite (Dresden University of Technology, ScaDS.AI Dresden/Leipzig, Germany)
• Prof. Julian Kunkel (University of Göttingen / GWDG, Germany)
• Prof. Chin-Chi Kuo (China Medical University Hospital, Taiwan)
• Prof. Sarah Neuwirth (Johannes Gutenberg University Mainz, Germany)
• Marlon Tobaben (University of Helsinki, LUMI AI Factory, Finland)