Unlike traditional resources such as CPU or the network, modern GPUs do not natively support fine-grained sharing primitives. Consequently, implementing common policies such as time-sharing and preemption are expensive. Worse, when a deep learning (DL) application cannot completely use a GPU’s resources, the GPU cannot be efficiently shared between multiple applications, leading to GPU underutilization. We present Salus to enable two GPU sharing primitives: fast job switching and memory sharing, to achieve fine-grained GPU sharing among multiple DL applications. Salus is an efficient, consolidated execution service that exposes the GPU to different DL applications, and enforces fine-grained sharing by performing iteration scheduling and addressing associated memory management issues. We show that these primitives can then be used to implement flexible sharing policies for various use cases. Our integration of Salus with TensorFlow and evaluation on popular DL jobs shows that Salus can improve the average completion time of DL training jobs by 3.19×, GPU utilization for hyper-parameter tuning by 2.38×, and GPU utilization of DL inference applications by 42× over not sharing the GPU and 7× over NVIDIA MPS with small overhead.
Peifeng Yu (University of Michigan)
Mosharaf Chowdhury (University of Michigan, Ann Arbor)
Related Events (a corresponding poster, oral, or spotlight)
2020 Oral: Fine-Grained GPU Sharing Primitives for Deep Learning Applications »
Tue Mar 3rd 10:30 -- 10:55 AM Room Ballroom A