Share-a-GPU: Providing Simple and Effective Time-Sharing on GPUs

Abstract

Time-sharing, which allows for multiple users to use a shared resource, is an important and fundamental aspect of modern computing systems. However, accelerators such as GPUs, that come without a native operating system do not support time sharing. The inability of accelerators to support time-sharing limits their applicability especially as they get deployed in Platform-as-a-Service and Resource-as-a-Service environmen ts. In the former, elastic demands may require preemption where as in the latter, fine-grained economic models of service cost can be supported with time sharing. In this paper, we extend the concept of time sharing to the GPGPU computational space using cooperative multitasking approach. Our technique is applicable to any GPGPU program written in Compute Unified Device Architecture (CUDA) API provided for C/C++ programming languages. With minimal support from the programmer, our framework incorporates process scheduling, light-weight memory management, and multi-GPU support. Our framework provides an abstraction where, in a round-robin manner, every workload can use a GPU(s) over a time quantum exclusively. We demonstrate the applicability of our scheduling framework, by running many workloads concurrently in a time sharing manner.