报告1题目：Energy Efficient Hardware Accelerators for Graph Processing and
Learning
报告时间：2024年5月29日15:00-15:45
报告地点：亚英体育八楼报告厅
报告人：何丙胜
报告人单位： National University of Singapore
报告人简介：
Dr. Bingsheng He is currently a Professor and Vice-Dean (Research) at School of Computing, National University of Singapore. His current research interests include cloud computing, database systems and high performance computing. He has been a winner for industry faculty awards from Microsoft/NVIDIA/Google/ Xilinx/Alibaba. His work also won multiple recognitions as “Best papers” collection or awards in top forums such as SIGMOD 2008, VLDB 2013 (demo), IEEE/ACM ICCAD 2017, PACT 2018, IEEE TPDS 2019, FPGA 2021 and VLDB 2023 (industry). Since 2010, he has (co-)chaired a number of international conferences and workshops, including IEEE CloudCom 2014/2015, BigData Congress 2018, ICDCS 2020 and ICDE 2024. He is an ACM Distinguished member (class of 2020).
报告摘要：
Graphs are de facto data structures for many data processing applications, and their volume is ever growing. Many graph processing tasks are computation intensive and/or memory intensive. Therefore, we have witnessed a significant amount of effort in accelerating graph processing tasks with heterogeneous architectures like GPUs, FPGAs and even ASIC. In this talk, we will first review the literatures of large graph processing systems on heterogeneous architectures. Next, we present our research efforts, and demonstrate the significant performance impact of hardware-software co-design on designing high performance graph computation systems and applications. Finally, we outline the research agenda on challenges and opportunities in the system and application development of future graph accelerators. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/.
邀请人：杜博、江佳伟、祝园园
报告2题目：面向新兴计算架构的高效图数据处理
报告时间：2024年5月29日15:45-16:30
报告地点：亚英体育八楼报告厅
报告人：孙世轩
报告人单位：上海交通大学
报告人简介：
孙世轩博士目前是上海交通大学计算机科学与工程系长聘教轨副教授。此前，在新加坡国立大学从事博士后研究员工作（2020-2023）。孙世轩于香港科技大学获得博士学位（2015-2020），同济大学获得本科和硕士学位（2007-2014）。他的主要研究方向是大数据系统和并行计算，目前专注于高性能图数据处理的研究；研究成果发表在SIGMOD、VLDB、ASPLOS、ICDE等顶级会议。他入选了国家级青年人才引进计划，上海市青年人才引进计划。
报告摘要：
作为有效建模和分析实体间关联关系的方式，图被广泛用于社交网络、在线支付、互联网等实际应用中。然而，图数据的海量性、稀疏性和异构性，以及图计算负载的多重动态性，为大规模图计算的性能和硬件资源的有效利用带来巨大挑战。为了应对上述挑战，我们着重研究面向新兴计算架构的图数据处理，基于图数据和计算负载特性，挖掘新兴计算架构的优势，提升系统的高效性。本次报告将介绍我们在基于Serverless架构和GPU加速的图数据处理方面的进展。
邀请人：杜博、江佳伟、祝园园
