AI on HPC: Performance Engineering, Challenges and Opportunities

Program

Time	Topic	Presenter
9:00–11:00	Invited Talk: performance inefficiencies in LLM fine-tuning	Gokcen Kestor, Barcelona Supercomputing, Spain
	PreLoRA: Hybrid Pre-training of Vision Transformers with Full Training and Low-Rank Adapters	Murali Emani, Argonne National Laboratory, USA
	MCast: Generalizing HPC Application Runtime Prediction	Avani Wildani, Cloudflare
	Panel Discussion: The Rise of HPC-Driven AI Factories Worldwide Panel Moderator: Vijeta Sharma, MIMER AI Factory, Sweden	Claudia Blaas-Schenner, AI Factory, Austria Thor Wikfeldt, MIMER AI Factory, Sweden Abdulrahman Azab, Norwegian AI Factory – Sigma2 AS, Norway Kerem Kayabay, HLRS/HammerHAI, Germany
11:00–11:30	☕ Coffee Break
11:30–13:00	An Autotuning-based Hyperparameter Optimization Framework for Mixed-kernel SVM Classifications in Science and Engineering	Xingfu Wu, Argonne National Laboratory, USA
	Resource-aware Computation-Communication Overlap for multi-GPU ML Workloads	Minyu Cui, Chalmers University of Technology, Sweden
	Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems	Adrian Perez Dieguez, Qualcomm, USA
	LLMs in Performance Engineering: New Workflows for Autonomous Code Optimization	Anja Gerbes, GWDG, Germany
13:00–14:00	🍲 Lunch Break
14:00–16:00	Keynote: Challenges and Opportunities in Training Next-Generation Mixture of Expert Models on HPC Platforms	Sajal Dash, Oak Ridge National Laboratory, USA
	TiledAttention: a CUDA Tile SDPA Kernel for PyTorch	Taimur Khan, UFZ, Germany
	Performance Engineering for the SEODA Project for CASPIr (Lightning Talk)	Buket Benek Gursoy, Irish Centre for High-End Computing, Ireland
	JumpLM: Simultaneous Visualization of Hardware and LLM Metrics for a Joint Configuration Tuning	Lena Jurkschat, ScaDS.AI, Germany
	AI Application Benchmarking: Power-Aware Performance Analysis for Vision and Language Models	Lukas Schröder, NHR@FAU, Germany
16:00–18:00	☕ Coffee Break / open discussions

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.

We invite submissions of original research papers, case studies, and experience reports that address the challenges and opportunities at the intersection of AI/ML and HPC. The papers submitted to this workshop will be published in LNCS, Springer.

Topics of Interest

We welcome submissions on the following topics, including but not limited to:

Workload Characterization

Characterizing AI/ML workloads on HPC systems
Data preparation for AI/ML workload on HPC
Hybrid workloads on HPC systems

Performance & Optimization

Parallelization strategies for AI/ML
Performance optimization of AI/ML frameworks on HPC
Efficient inference of LLMs on HPC
Cross-platform portability and reproducibility

Infrastructure & Systems

AI factories and end-to-end pipelines
Next-generation HPC systems for AI/ML
Best practices for integrating ML/AI into HPC
Specialized AI/ML frameworks for HPC

Resource Management

Resource allocation and scheduling for AI/ML workloads
Energy efficiency and power management
DevOps and MLOps for HPC-AI/ML

Applications

HPC-AI/ML convergence for scientific applications
AI-enhanced HPC simulations
Industrial AI/ML on HPC
Collaborative and interactive AI/ML on HPC

Evaluation & Benchmarking

HPC-AI/ML benchmarking and evaluation
Performance studies and best practices

Important Dates

Submission Deadline (firm) March 15, 2026 AOE

Notification of Acceptance April 16, 2026

Early-bird registration Deadline May 6, 2026 AOE

Camera-Ready Deadline May 11, 2026 AOE

Submission Information

→ Format: LNCS (Lecture Notes in Computer Science) format

→ Length: 6 to 12 pages for full papers (including a bibliography and appendices)

→ Lightning Talks: Maximum 1 page abstract (talks excluded from publication)

→ Portal: ISC Submission portal

→ Submissions should be original and not previously published

→ Download LNCS template from Springer

→ Submissions may be either single-blind or double-blind and authors can decide whether to anonymise their names and affiliations.

→ For questions about submissions, please contact the General chairs.

Start Submission →

Workshop Organization

General Chairs

Dr. Siavash Ghiasvand — Dresden University of Technology, ScaDS.AI Dresden/Leipzig, Germany
Dr. Vijeta Sharma — MIMER AI Factory, Sweden
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
Prof. Dr. Faouzi Alaya Cheikh — Norwegian University of Science and Technology, Norway
Dr. Ernst Gunnar Gran — Norwegian University of Science and Technology, Norway

Program Committee

Prof. Victor Calo — Curtin University, Australia
Prof. Florina M. Ciorba — University Basel, Switzerland
Dr. Jens Domke — RIKEN, Japan
Dr. Christian Engelmann — Oak Ridge National Laboratory, USA
Robert Henschel — Indiana University, USA
Dr. Rene Jäkel — Dresden University of Technology, ScaDS.AI Dresden/Leipzig, Germany
Dr. Thor Wikfeldt — RISE, Sweden
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 — CSC, LUMI AI Factory, Finland
Dr. Yonglei Wang — Linköping University, Sweden

Media Chair

Beyza Çavuşoğlu

Contact

For questions, please contact the General Chairs.