Large Language Models: Finding Their Place in the HPC Ecosystem

Traditional HPC architectures have typically been designed to address either homogenous workloads (such as physics-based modeling and simulation) with similar, and perhaps more important, fixed, compute, memory, and I/O requirements or, more recently, heterogenous workloads with a diverse range of compute, memory, and I/O requirements. Most HPC data center planners and operators, however, don't have the luxury of focusing on one main type of workload; they typically must support many HPC users and their associated workloads sporting a wide range of compute, memory, and I/O profiles. Architectures have typically consisted of a fixed set of resources, resulting in an underutilized system with expensive elements often sitting idle for long periods of time. One approach being explored to increase system utilization by exposing resources that would otherwise sit idle to appropriately matched jobs waiting in a queue is via composable systems.

With over 11,000 on-site attendees approaching pre-pandemic levels, and only 700 virtual participants, SC22 in Dallas, TX far exceeded the high expectations of the broad HPC community. There was no shortage of avenues for participants to obtain the latest knowledge relative to market developments and technology innovations occurring across the industry. The Hyperion Research team of analysts has compiled its primary takeaways and perspectives from the event.