AI on HPC: Performance Engineering, Challenges and Opportunities
Friday, June 26, 2026 9:00 AM to 6:00 PM · 9 hr. (Europe/Berlin)
Hall X6 - 1st Floor
Workshop
AI Applications powered by HPC TechnologiesAI FactoriesIndustrial Use Cases of HPC, ML and QCLarge Language Models and Generative AI in HPCML Model Optimization
Information
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.
Organizers:
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.
Organizers:
Format
on-site
Targeted Audience
Researchers, industrial partners, and practitioners interested in the convergence of HPC and AI/ML. This workshop aims to foster collaboration and knowledge sharing among academia, industry, and research institutions to accelerate the development of HPC-AI/ML synergies and facilitate AI/ML technology adoption.
Beginner Level
30%
Intermediate Level
40%
Advanced Level
30%
Speakers
Abdulrahman Azab
Subject Manager - Advanced ComputingNorwegian Research Infrastructure Services (NRIS), Sigma2 AS
Murali Emani
Computer ScientistArgonne National LaboratoryGK
Gokcen Kestor
Senior ResearcherBarcelona Supercomputing Center
Siavash Ghiasvand
Senior researcherTechnische Universität Dresden, ScaDS.AI Dresden/Leipzig
Ajeet Ram Pathak
Postdoctoral FellowNorwegian University of Science and Technology
Vijeta Sharma
HPC-AI ScientistSwedish National AI Factory-MIMERAG
Anja Gerbes
Scientific ResearcherGWDG
Sajal Dash
Research ScientistOak Ridge National Laboratory
Kerem Kayabay
Senior ManagerHLRSLS
Lukas Schröder
ResearcherNHR@FAU