Invited Talks


Memory: The DaMoN Demon

Frank Hady, Intel Fellow, Intel’s Office of the CTO Systems Architecture and Engineering Group


Abstract: Memory technology limitations bedevil current computing systems and data management does not escape. As silicon scaling delivers ever faster compute, memory falls further behind exposing capacity, bandwidth, and power deficiencies in our systems. Seeing these issues, computer architects propose memory hierarchy changes only to find most applications shrink-wrapped to the current hierarchy and unable to change. Data management applications provide an innovation bright-spot with researchers and developers ready to co-optimize from application-to-hardware to deliver improved performance. Hierarchy improvements often matter here first. Sitting squarely at this confluence, DaMoN serves to engender such co-optimizations. With this in mind, we will set a memory technology baseline using memory silicon trends and constraints. Additionally, we will set a memory system baseline summarizing current system memory architectures and issues. Next, we will look at the undeniable influence AI is exerting on the hierarchy. Taking memory technology, systems, and applications together, we will speculate on memory hierarchy changes to expect – potentially creating opportunities for future data management applications. One such change is already visible in CXL-enabled memory hierarchies envisioned to deliver higher capacity and perhaps more. Finally, we will speculate further on system optimizations around memory that are the subject of current research, like memory sharing and near memory computing. Active audience engagement is encouraged, as the goal of this presentation is a maximally productive DaMoN focused on vanquishing the memory demon.


Frank Hady is an Intel Fellow responsible for memory and storage hierarchy innovation within Intel’s Office of the CTO Systems Architecture and Engineering Group. He is a long-time system researcher happiest when delivering innovations that span hardware and software. Over Frank’s three-decade career, he has contributed to the creation, delivery, and proliferation of fundamental systems technologies. His current focuses include memory hierarchy advances, near memory compute systems pathfinding, and the optimization of storage for AI. Frank was a founding member of the team that delivered Intel® Optane™ technology and architected the first products. He helped Intel build a successful storage business, directing overall storage pathfinding and architecture, and architecting Intel’s first NVMe SSDs. His systems contributions include research foundational to Intel’s heterogeneous compute platforms and key I/O interfaces. In networking, he has delivered industry benchmarks and built a supercomputer network.  He has held cross-Intel leadership roles for both I/O and memory architecture. Frank has authored or co-authored numerous published papers and patents, and presents often on memory and storage. Frank earned a BS and MS in Electrical Engineering from the University of Virginia, and Ph.D. in Electrical Engineering from the University of Maryland.


Cost-Intelligent Data Analytics in the Cloud
Huanchen Zhang, Assistant Professor, IIIS (Yao Class) at Tsinghua University  

Abstract: For decades, database research has focused on optimizing performance under a fixed amount of resources. As more and more database applications move to the public cloud, we argue that it is time to make cost a first-class citizen when solving database optimization problems. In this talk, I will introduce the concept of "cost intelligence" and then sketch the architecture of a cloud data warehouse designed toward this goal. The project is in its early stages, and we would appreciate your valuable feedback.


Huanchen Zhang is an Assistant Professor in the IIIS (Yao Class) at Tsinghua University. His research interest is in database management systems with particular interests in indexing, data compression, and cloud databases. He received his Ph.D. degree from the Computer Science Department at Carnegie Mellon University. Before joining Tsinghua, he worked at Snowflake as a Postdoctoral Research Fellow. He is the recipient of the SIGMOD Jim Gray Dissertation Award (2021) and the SIGMOD Best Paper Award (2018).