ABSTRACTS

HPC in Transition

Jack Dongarra
Professor emeritus, Innovative Computing Laboratory, EECS Department
University of Tennessee,  Knoxville, TN

High-performance computing is entering a decisive transition driven by forces that are largely external to traditional scientific HPC. The economics of AI and hyperscale cloud now shape leading-edge silicon, system architectures, and software ecosystems, while energy and data movement have become the dominant constraints on performance, facility design, and long-term sustainability. This talk examines how these dynamics shift HPC’s center of gravity from a primarily FP64, node-centric worldview toward accelerator-heavy, rack-scale, and workflow-defined systems. We argue that the next era of scientific capability will be measured less by peak floating-point rates and more by time–energy–fidelity trade-offs across end-to-end pipelines. The most plausible path to “effective zettascale” is not brute-force FP64, but certified mixed-precision algorithms, communication-avoiding methods, AI-augmented reduced-order models, and hybrid AI+simulation workflows with rigorous error control and uncertainty quantification. We also outline an emerging reference architecture for platforms comprising integrated simulation, AI, and data/workflow partitions, linked and coordinated across multiple separate resources with secure cloud resources and instruments.