European Parliament Endorses EuroHPC Exascale Initiative

Expertise is now a major concern for both HPC and AI users, outranked only by budget concerns among top barriers to expanding HPC on-premises. A third of HPC sites (33.1%) reported that lack of knowledge or skilled support staff was one of their top three barriers. Only a third of respondents (35.4%) report that they do not have any staffing concerns within the next year. When asked about barriers to furthering AI capabilities, AI-specific expertise was identified as a significant concern. Other significant barriers also included access to AI expertise (48.9%), skills in AI model development (47.2%), and skills in AI programming (36.1%). This data is from an annual study that is part of the eighth edition of Hyperion Research's HPC end-user-based tracking of the HPC marketplace. It included 181 HPC end-user sites with 3,830 HPC systems.

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.