2021 Use of Public/External Clouds for HPC Workloads, Trends, and Drivers

According to a recent Hyperion Research study of 135 US quantum computing (QC) technology developers across the academic, commercial and government sectors, the global QC market was worth about $250 million (+/- $30 million) in 2019. Based on an anticipated CAGR of 27% between 2020 and 2024, the global QC market will grow from approximately $320 million in 2020 to $830 million in 2024. On-prem and cloud access QC hardware will comprise about 50% of the global QC market for the next three years, while optimization, physical simulation, and machine learning will near equally divide the algorithm space. This is Hyperion Research's baseline estimate for the rapidly evolving market, and Hyperion Research anticipates providing periodic updates as events, information, and decision-making requirements within the sector dictate.

HPC cloud adoption has grown aggressively over the last few years as cloud providers have recognized the HPC market as a high value target and have brought HPC-specific capabilities to their platforms to entice users. This has resulted in growing number of HPC users that are now using external clouds for parts of their HPC workload portfolio.   Hyperion Research has followed the growth vector of cloud adoption for many years and conducted a number of broad studies to better understand the underlying trends of cloud usage. This research has shown that users are contemplating different approaches of cloud adoption including what types of applications to send to the cloud; how much to send to the cloud; and ultimately whether to run a specific workload in the cloud or not.   As the performance capabilities of the cloud for HPC workloads improve, many of the barriers to adoption have been reduced, but some remained consistent barriers despite the technological advancements. These adoption inhibitors may be offset in many situations by the corresponding drivers and benefits. This will lead to increased cloud usage for many HPC applications and workloads in both traditional modeling/simulation as well as artificial intelligence.