Presentation
MIGnificient: Fast, Isolated, and GPU-Enabled Serverless Functions
DescriptionEmerging applications in machine learning and personalized medicine introduce new challenges and requirements for secure computing. While the exclusive allocation of resources to a single tenant provides necessary isolation, it also comes at the cost of hardware underutilization. While solutions like containers allow for secure sharing of CPUs, new techniques are needed to efficiently co-locate applications on GPUs. We propose a new approach that merges the elasticity of the Function-as-a-Service (FaaS) with the physical GPU partitioning of NVIDIA MIG. In MIGnificient, we provide spatial isolation through concurrent execution on different device partitions, preventing side-channel attacks and performance interference. We employ local API remoting that controls kernel scheduling and memory transfers, enabling compute-communication overlap and improved resource management in virtualized API. MIGnificient overcomes the limitations of state-of-the-art solutions that rely on slower network-based API remoting and insecure NVIDIA MPS, creating a unifying model for optimized serverless GPU functions.
