主题：High-Performance Graph Data Systems: Lessons Learned and Future Directions

嘉宾：何丙胜 新加坡国立大学  教授  计算机学院副院长

时间：2024年11月14日 下午15:00 – 16:00

地点：华中科技大学东五楼210学术报告厅

报告摘要：

Graph data structures are fundamental to numerous data processing and learning applications. Over the past decades, the size, diversity, and complexity of graph data have grown significantly, presenting a wide array of challenges from processing to learning. This has spurred extensive research in the field, with performance emerging as a critical factor. Throughout this period, we have developed a variety of graph data systems, ranging from parallel graph processing systems on heterogeneous hardware like GPU, FPGA and new architectures to applications in cryptocurrency and e-commerce. In this talk, I will share our journey in building high-performance graph data systems, summarize the lessons we have learned, and outline future directions for this field. More details about our research can be found at http://www.comp.nus.edu.sg/~hebs/.

报告人简介:

Dr. Bingsheng He is currently a Professor and Vice-Dean (Research) at School of Computing, National University of Singapore. Before that, he was a faculty member in Nanyang Technological University, Singapore (2010-2016), and held a research position in the System Research group of Microsoft Research Asia (2008-2010), where his major research was building high performance cloud computing systems for Microsoft. He got the Bachelor degree in Shanghai Jiao Tong University (1999-2003), and the Ph.D. degree in Hong Kong University of Science & Technology (2003-2008). 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/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 has served in editor board of international journals, including IEEE Transactions on Cloud Computing (IEEE TCC), IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS), IEEE Transactions on Knowledge and Data Engineering (TKDE), Springer Journal of Distributed and Parallel Databases (DAPD) and ACM Computing Surveys (CSUR). He is an ACM Distinguished member (class of 2020).