GSLAC: GPU Software Level Access Control for Information Isolation on Cloud Platforms

The massive parallel architecture makes Graphics Processing Unit (GPU) a powerful accelerator for various computational intensive tasks, such as computer games, scientific computation, cryptocurrency, and AI model training and inferences. In many cloud platforms, GPUs are scarce computing resources and shared by multiple users. To achieve information isolation among different user programs, GPU access control is an essential technology to prevent the information leaking for program execution and data access when using GPUs. However, the lack of a zeroing mechanism in GPUs, combined with vulnerabilities in user-land drivers, poses risks to both data confidentiality and system integrity. In this paper, we propose a novel system architecture, called GSLAC, to provide GPU System Level Access Control for information isolation on cloud platforms. GSLAC combines resource isolation and mandatory access control measures with the aim of establishing a secure computing environment. It encompasses an authentication mechanism for authorized GPU access, as well as the integration of mandatory access control mechanisms to safeguard sensitive resources. Furthermore, with a careful design, user programs can be compiled and executed as they do in a normal environment without sacrifying the desired performance.