Gemini: Enabling Multi-Tenant GPU Sharing Based on Kernel Burst Estimation
Hung‐Hsin Chen, En-Te Lin, Yu-Min Chou, Jerry Chou · IEEE Transactions on Cloud Computing · 2021
Recent years have seen rapid adoption of GPUs in various types of platforms because of the tremendous throughput powered by massive parallelism. However, as the computing power of GPU continues to grow at a rapid pace, it also becomes harder to utilize these additional resources effectively with the support of GPU sharing. In this work, we designed and implementedGemini, a user-space runtime scheduling framework to enable fine-grained GPU allocation control with support for multi-tenancy and elastic allocation, which are critical for cloud and resource providers. Our key idea is to introduce the concept ofkernel burst, which refers to a group of consecutive kernels launched together without being interrupted by synchronous events. Based on the characteristics of kernel burst, we proposed a low overheadevent-driven monitorand adynamic time-sharing schedulerto achieve our goals. Our experiment evaluations using five types of GPU applications show that Gemini enabled multi-tenant and elastic GPU allocation with less than 5% performance overhead. Furthermore, compared to static scheduling, Gemini achieved 20%$\sim$30% performance improvement without requiring prior knowledge of applications.