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

As the world's two largest economies and recognized world-class science and technology (S&T) developers, China and the United States are aware of how important technological prowess has become in projecting geopolitical leadership from an economic and national security perspective, and each place significant weight on national S&T polices to help ensure those ends. Recently, the leadership of both China and the United States had occasion to make public their long[1]term strategic S&T visions:

• China's President Xi Jinping highlighted that China is seeking to replace the US as the acknowledged global leader in S&T capability, and Beijing will likely continue its on-going efforts to transform its overall S&T infrastructure from that of a fast follow nation to one of global leadership while reducing its dependency on foreign S&T sources.
• US President Joe Biden emphasized that although the US has a long legacy of world-class technological prowess, emphasis should shift to better position S&T progress to help support key societal imperatives including public health, climate change, and economic inclusiveness. In additional, President Biden charged policymakers with fostering renewed support for R&D funding that not only ensures US S&T primacy going forward but that also fosters the wide base of existing and potential new US commercial entities to quickly turn research breakthroughs into innovative products.

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.