報告題目：Algorithm and System Co-Optimized Big Data Analytics
報告時間：2021年12月30日 10:00-11:00 (GMT+08:00)
報告方式：ZOOM會議
會議碼：956 0022 9256
登陸密碼：724608
報告人：劉航
報告人簡介：
Dr. Hang LIU is currently an assistant professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. He received his Ph.D. degree from George Washington University in 2017, and B.E. from Huazhong University of Science and Technology in 2011. Hang LIU’s research interests include exploiting emerging hardware to build high-performance systems for graph computing, machine learning, data compression, numerical simulation, cloud computing, and software debugging. His publications appear in top-tier conferences, such as SC, VLDB, SIGMOD, ICDE, HPDC, USENIX FAST, USENIX ATC, and DAC. He is the recipient of the prestigious NSF CAREER Award, NSF CRII Award, DOE SRP fellowship, and the Champion of DARPA/MIT/AMAZON Graph Challenge 2018 and 2019. He is also the winner of the Best Dissertation Award from the Department of Electrical and Computer Engineering at George Washington University.
報告内容簡介：
Increasingly, people are awash in data, as a growing array of “sensors” that are integrated into our daily life, continues to generate an explosive amount of data. According to a recent study by IBM, we are creating ~2.5 quintillion bytes of data per day. Buried in such a rapidly growing influx of data are the key insights to address critical issues in our society, such as improving productivity, enlisting new economic opportunities, and uncovering novel discoveries in science and engineering. In the High-Performance Data Analytics (HPDA) Lab at Stevens Institute of Technology, we develop novel algorithms and systems to rapidly analyze the gigantic real-world data, understand the contextual and causal relationships within entities and events, and deliver actionable knowledge to stakeholders in real-time. In this talk, I will share our experiences in designing and developing high-performance systems for addressing the computational and I/O challenges faced in structured graph data (featured at SC '20). In addition, I will present our ongoing work on utilizing tensor-core-enabled hardware to accelerate emerging attention-based neural network models for sequence data (featured at SC '21).
主辦單位：伟德国际BETVlCTOR
伟德国际BETVlCTOR軟件學院
伟德国际BETVlCTOR計算機科學技術研究所
符号計算與知識工程教育部重點實驗室
仿真技術教育部重點實驗室
網絡技術及應用軟件教育部工程研究中心
伟德国际BETVlCTOR國家級計算機實驗教學示範中心
