As Moore’s law continues to slow, delivering more powerful HPC and AI clusters means building larger, more power hungry facilities.
“If you want more performance, you need to buy more hardware, and that means a bigger system; that means more energy dissipation and more cooling demand,” University of Utah professor Daniel Reed explained as a recent session at the SC23 supercomputing conference in Denver.
Today, the largest supercomputing clusters on the Top500 are consuming more than 20 megawatts and many datacenter campuses, particularly those built to support demand for AI training and inference, are even larger. Some projections suggest that by 2027 a capability-class supercomputer will require on the order of 120 megawatts of power.
During a panel on carbon-neutrality and sustainability in high-performance computing, experts from the University of Chicago, Schneider Electric, Los Alamos National Laboratory, Hewlett Packard Enterprise, and the Finnish IT Center for Science weighed in on these trends and offer their insights as to how we should be planing, deploying, reporting, and operating these facilities moving forward.
One of the overarching themes of the conversation was with regard to power use efficiency (PUE). For reference, the industry standard metric measures how efficient a datacenter is by comparing the amount of power used by compute, storage, or networking equipment against total utilization. The closer the PUE is to 1.0, the more efficient the facility.