Speakers

Dr. Xian-He Sun is a University Distinguished Professor, the Ron Hochsprung Endowed Chair of Computer Science, and the director of the Gnosis Research Center for AI-driven data management at the Illinois Institute of Technology (Illinois Tech). Before joining Illinois Tech, he worked at DoE Ames National Laboratory, at ICASE, NASA Langley Research Center, at Louisiana State University, Baton Rouge, and was an ASEE fellow at Navy Research Laboratories. Dr. Sun is an IEEE fellow and is known for his memory-bounded speedup model, also called Sun-Ni’s Law, for scalable computing. His research interests include high-performance data management, memory and I/O systems, and performance evaluation and optimization. He has over 350 publications and 7 patents in these areas and is currently leading multiple large software development projects in high performance data management systems. Dr. Sun is the Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems, and a former department chair of the Computer Science Department at Illinois Tech. He received the Golden Core award from IEEE CS society in 2017, the ACM Karsten Schwan Best Paper Award from ACM HPDC in 2019, and the first prize best paper award from ACM/IEEE CCGrid in 2021. More information about Dr. Sun can be found on his web site www.cs.iit.edu/~sun/.

The recent success of large language models is closely tied to the scaling law—that is, increasing the amount of data and computing power consistently yields more accurate solutions. The repeated validation of this scaling behavior motivates a renewed examination of scalable computing, the long-standing memory-wall problem, and the intertwined evolution of technology and applications. In this talk, we first review several turning points in scalable computing, highlighting their key discoveries and impacts. Drawing on the lessons learned and the emerging AI challenges, we then present our scalable high‑performance data management solution under the von Neumann architecture. Two NSF‑supported cyberinfrastructure systems—Hermes and ChronoLog—are used to illustrate this solution and demonstrate its effectiveness. Hermes focuses on technology‑driven memory‑wall issues, whereas ChronoLog addresses the application‑driven data‑log challenge, where AI technology is applied to timing for the first time. Finally, we will discuss our ongoing IOWrap data management system for AI applications, which is shaped by both technological advances and application needs.