Efficient GPU Sharing for Serverless Workflows

Abstract

Serverless computing has emerged as a new cloud computing paradigm, where an application consists of individual functions that can be separately managed and executed. However, the function development environment of all serverless computing frameworks at present is CPU-based. In this paper, we propose to extend the open-sourced KNIX high-performance serverless framework so that it can execute functions on shared GPU cluster resources. We have evaluated the performance impacts on the extended KNIX system by measuring overheads and penalties incurred using different deep learning frameworks.