Perspectives on Sustainability in HPC: Current Views and Future Considerations

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.

A part of the annual Hyperion Research HPC Multi-Client study (MCS) tracks the various application frameworks respondents use on their HPC systems to discover trends in user preference for the way applications are built and deployed, as well as trends in the functionalities users desire in their solutions and products. This year's iteration of the MCS found the most popular AI/ML/DL and data intensive frameworks were TensorFlow, PyTorch, Jupyter Notebook, SQL, CUDA, Hadoop, and Spark. 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.