Advancing HPC in an AI Driven World: From Coexistence to Convergence

AI is changing how high performance computing systems are designed, operated, and consumed—but it is not displacing HPC. Instead, AI is forcing a reassessment of long standing HPC assumptions around workload scheduling, GPU utilization, storage architecture, and system economics. As large scale AI workloads move into production, organizations must reconcile the flexibility demanded by AI with the rigor required for reliable, cost effective HPC operations.

This presentation explores how HPC platforms are evolving to remain relevant and effective in an AI driven world. We'll examine practical architectural patterns supporting both traditional simulation workloads and modern AI training and inference on shared infrastructure. Key topics include GPU cluster design for mixed workloads, integrating AI workflows into HPC schedulers, emerging data access patterns that challenge existing storage models, and the role of hybrid and on premises platforms in addressing data gravity, cost predictability, and scale.

Rather than focusing on individual technologies or vendor trends, the talk emphasizes system level decisions, trade offs, and operational considerations faced by HPC operators today. It highlights where established HPC principles—determinism, reproducibility, and governance—remain essential, and where adaptation is required to support AI workloads sustainably.

Attendees will gain a pragmatic view of how HPC continues to advance in the AI era: not by becoming something entirely new, but by evolving proven architectures and operational practices to meet emerging computational demands.