报 告 人：陈全 Tenure-Track特别研究员  上海交通大学，计算机科学与工程系    

报告时间：05月11日（周五）13:30～15:00

报告地点：宝山校区计算机大楼1004室

邀 请 人：童维勤 教授

报告摘要：

  Modern private datacenters are being outfitted with accelerators to provide the significant compute requiredby emerging online services. It is well known that the diurnal user access pattern of user-facing services provides a strong incentive to co-locate applications for better accelerator utilization and efficiency, and prior work has focused on enabling co-location on multicore processors. However, interference when co-locating applications on non-preemptive accelerators is fundamentally different than contention on multi-core CPUs a-nd introduces a new set of challenges to reduce QoS violation. In this talk, I will introduce our Baymax s-ystemthat improves the accelerator utilization in private datacenters while guaranteeing that user-facing servic-es achieve the required Quality-of-Service. 

  Meanwhile, GPUs have also been adopted in public Clouds. However, performance fairness among concur-rent applications on GPU, which is critical in public multi-tenant Clouds, is minimally supported. Targeting the public Clouds, I will introduce an machine learning-based runtime system that enables the fair sharing inpublic Clouds without any prior knowledge of user programs.

报告人简介:

陈全博士现为上海交通大学计算机科学与工程系Tenure-Track特别研究员。主要研究方向包括计算机系统、计算机体系结构、数据中心资源管理等。陈全已发表英文专著1部、论文超过30篇，多数发表在Science（特刊）、IEEE Trans、ACM Trans等著名期刊以及ASPLOS、ISCA、IPDPS、ICS等计算机系统领域顶级会议上。其论文获系统领域顶级会议ASPLOS 2017 Highlights，为国内学者第2次获此奖项。其研究在相关领域得到广泛关注，Google Scholar引用超过1500次，单篇最高引用达1140次。陈全曾获2015年CCF优博、2016年上海市优博、2017年教育部自然科学一等奖（排名：3/3）、2017年IEEE TCSC Award for Excellent (Early Career Researcher) 等多项荣誉，并于2017年入选微软铸星计划。