Expertise is a Major Concern for Both HPC and AI Users

Key findings from a recent Hyperion Research study indicate that HPC storage solutions, and associated storage and system interconnects, continue to be critical for HPC infrastructure to deliver optimal capabilities and provide the fastest time to results for the systems' users. Data-intensive workloads driven by new AI/ML/DL workloads, increasing scale of traditional HPC modelling and simulation, emerging edge computing, and emerging composable systems are placing greater demands and requirements on HPC storage systems. Insights into the critical factors driving these and other trends are detailed in the 2022 iteration of Hyperion Research's annual MCS end users' study, 2022 HPC Multi-Client Study: Trends and Forecasts in HPC Storage and Interconnects. Key findings from the report are summarized in this document.

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.