Moderator: Daniel Reed (University of Utah)
Panelists: Dorian Arnold (Emory University), Jack Dongarra (University of Tennessee, Innovative Computing Laboratory), Torsten Hoefler (ETH Zürich), Katherine Yelick (University of California, Berkeley)
Abstract: Today, most HPC systems on the TOP500 are examples of a commodity monoculture, built from nodes containing server-class microprocessors and GPU accelerators. With the end of Dennard scaling, the slowing of Moore’s Law, and exponentially rising costs for semiconductor fabrication facilities, high-performance computing (HPC) is at an important inflection point. In another profound shift, computing economics are now dominated by cloud hyperscalers and smartphone vendors who are increasingly building using custom semiconductors. Concurrently, AI advances are reshaping how we think about the nature of scientific computation and pursue scientific breakthroughs.
How can the HPC community best adapt? Our thesis is that current approaches to designing and constructing leading edge high-performance computing systems must change in fundamental ways. This panel will explore possible approaches based on end-to-end co-design; custom hardware configurations and packaging; large-scale prototyping, which was once common; and collaborative partnerships; motivated in part by a recent position paper: https://arxiv.org/abs/2203.02544