Session

Description HPC continues to be commoditised and democratised, with HPC-as-a-service, workflow-based HPC, and a growing number of HPC and AI use cases, whilst the largest scale computing resources are simultaneously concentrated into fewer sites capable of meeting the vast power, infrastructure, and financial requirements of exascale systems. As such, HPC service operators are required to cater to an ever-widening variety of users with diverse workloads and potentially sensitive data, and so the ability to protect and isolate confidential workloads in multi-tenant HPC environments is becoming increasingly important. There exist many different HPC or near-HPC workloads which currently cannot run on public or federated cloud environments for various reasons. While the concerns often originate from legal aspects, such as regulatory requirements, protection of intellectual property (algorithms or data) or protection of personal data, the solution must be provided on the platform level. In this minisymposium, we intend to capture the current state of Confidential Computing in HPC, ranging from direct application and workflows to deployment and low-level implementation. We illustrate the impact of mitigation techniques on HPC architectures. We also explore possible advancements and alternatives to silicon-based trust regions, by looking at more fundamental mathematical topics, such as Homomorphic Encryption.