This workshop examines how to address the growing misalignment between frontier-scale AI models and the realities of contemporary high-performance computing systems. It focuses on the requirements for running trillion-parameter training, large-scale synthetic data pipelines, and multilingual scientific workflows reliably on modern supercomputers. The discussion emphasizes practical integration: how HPC schedulers, storage systems, networking, and software stacks must evolve to support foundation-model development, how to coordinate shared synthetic-data and benchmarking infrastructure under initiatives such as the Trillion Parameter Consortium; and how emerging AI techniques are beginning to assist HPC itself by enabling automatic code generation, performance optimization, and realistic workload simulation. By treating AI and HPC as mutually reinforcing domains, the workshop aims to articulate a concrete path toward systems, tools, and operational practices capable of sustaining the next generation of large-scale scientific computing.
This workshop provides a platform for experts from academia, government, and industry to explore these challenges, share recent advances, and discuss emerging opportunities in AI-driven scientific discovery. The workshop aims to bridge the gap between AI research and scientific applications by bringing together computer scientists, domain experts, and HPC practitioners to exchange ideas and foster collaboration. By showcasing cutting-edge AI/ML advancements, identifying key challenges in deploying AI at scale, and facilitating discussions on the future of AI in science, this workshop will serve as a catalyst for accelerating AI adoption in scientific research.
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