gScale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics Memory Space Mochi Xue, Shanghai Jiao Tong University and Intel Corporation; Kun Tian, Intel Corporation; Yaozu Dong, Shanghai Jiao Tong University and Intel Corporation; Jiacheng Ma, Jiajun Wang, and Zhengwei Qi, Shanghai Jiao Tong University; Bingsheng He, National University of Singapore; Haibing Guan, Shanghai Jiao Tong University https://www.usenix.org/conference/atc16/technical-sessions/presentation/xue This paper is included in the Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC ’16). June 22–24, 2016 • Denver, CO, USA 978-1-931971-30-0 Open access to the Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC ’16) is sponsored by USENIX. gScale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics Memory Space Mochi Xue1,2, Kun Tian2, Yaozu Dong1,2, Jiacheng Ma1, Jiajun Wang1, Zhengwei Qi1, Bingsheng He3, Haibing Guan1 {xuemochi, mjc0608, jiajunwang, qizhenwei, hbguan}@sjtu.edu.cn {kevin.tian, eddie.dong}@intel.com
[email protected] 1Shanghai Jiao Tong University, 2Intel Corporation, 3National University of Singapore Abstract As one of the key enabling technologies of GPU cloud, GPU virtualization is intended to provide flexible and With increasing GPU-intensive workloads deployed on scalable GPU resources for multiple instances with high cloud, the cloud service providers are seeking for practi- performance. To achieve such a challenging goal, sev- cal and efficient GPU virtualization solutions. However, eral GPU virtualization solutions were introduced, i.e., the cutting-edge GPU virtualization techniques such as GPUvm [28] and gVirt [30]. gVirt, also known as GVT- gVirt still suffer from the restriction of scalability, which g, is a full virtualization solution with mediated pass- constrains the number of guest virtual GPU instances.