How Persistent Memory Changes the Server Environment

Andy Rudoff (Intel)

New memory technologies bring with them an explosion in memory capacities, offering multiple terabytes per CPU socket. But more than that -- this new, large capacity memory is persistent! Andy will describe how this technology changes the server environment seen in data centers and clouds. He will explain the value of persistent memory, what it means to applications such as databases, and summarize what application vendors are doing to prepare for it. Andy will describe the work done by SNIA, the Storage Networking Industry Association, to align the industry on a unified programming model for persistent memory. He’ll show libraries and applications that have built on that model and describe the value they’ve demonstrated.

Andy Rudoff is a Senior Principal Engineer at Intel Corporation, focusing on Non-Volatile Memory programming. He is a contributor to the SNIA NVM Programming Technical Work Group. His more than 30 years industry experience includes design and development work in operating systems, file systems, networking, and fault management at companies large and small, including Sun Microsystems and VMware. Andy has taught various Operating Systems classes over the years and is a co-author of the popular UNIX Network Programming text book.

Fresh Thinking: New Researchers, Bright Ideas

A series of invited talks by younger researchers, including:

Active Heterogeneous Hardware and its Impact on System Design

Jana Giceva (Imperial College)

The rise of hardware heterogeneity and the potential to offload compute closer to data (e.g., storage and memory) or to push operations down to where data moves (e.g., on the networks or acceleration within the chip) opens both exciting opportunities and significant challenges for system software like databases that want to make efficient use of future hardware. One of the main questions is then, who absorbs that complexity especially as we move to the "noisy" cloud? In my talk, I will argue that addressing such a challenge requires an effort that is beyond what can be typically done within a single layer of the system stack. My proposal calls for a holistic approach by opening up the interfaces and customising the system stack for modern data processing workloads.

Jana Giceva is an assistant professor in the Department of Computing at Imperial College London, where she is part of the LSDS (Large Scale Data and Systems) group. Prior to that she completed her MSc and Phd in the Systems Group at ETH Zurich, where she was advised by Gustavo Alonso and co-advised by Timothy Roscoe. Her research interests are in systems support for Big Data and Data science to enable efficient use of modern and future hardware. The scope of her research spans multiple systems areas: from the data processing layer to operating systems, including hardware accelerators for data processing. She is the recipient of the ETH medal for her PhD dissertation awarded in 2017 and the Google European PhD Fellowship in operating systems in 2014.

Scaling database systems to high-performance computers

Spyros Blanas (Ohio State)

Analyzing massive datasets quickly requires scaling foundational data processing algorithms to the unprecedented compute, network and I/O concurrency of a modern datacenter. However, the software building blocks that are readily available today have largely been designed for high-performance computing applications and are profoundly unsatisfactory for I/O-intensive analytics. This talk highlights specific research challenges that need to be overcome to scale data processing to warehouse-scale computers, with particular focus on how to better utilize RDMA-capable networks, non-uniform network topologies, massively parallel file systems and NVMe-based storage in a disaggregated datacenter.

Spyros Blanas is an assistant professor in the Department of Computer Science and Engineering at The Ohio State University. His research interest is high-performance database systems, and his current goal is to build a database system for high-end computing facilities. He has received the IEEE TCDE Rising Star Award and a Google Research Faculty award. He received his Ph.D. at the University of Wisconsin–Madison and part of his Ph.D. dissertation was commercialized in Microsoft's flagship data management product, SQL Server, as the Hekaton in-memory transaction processing engine.

Designing Data Management Systems in the Age of Dark Silicon

Pinar Tözün (IT University of Copenhagen)

Dennard scaling, which enables keeping the power density of the transistors constant, does not hold anymore. Even though we would be able to keep packing more cores in processors, we won’t be able to power all of them up simultaneously. This trend is referred to as dark silicon and fundamentally alters the focus of hardware design. In this new era, the focus needs to shift toward optimizing energy per instruction. This talk focuses on the implications of dark silicon and emerging hardware on the design of data management systems.

Pınar Tözün is an Associate Professor at IT University of Copenhagen. Before ITU, she was a research staff member at IBM Almaden Research Center. Prior to joining IBM, she received her PhD from EPFL. Her research focuses on HTAP engines, performance characterization of database workloads, and scalability and efficiency of data management systems on modern hardware. She received a Jim Gray Doctoral Dissertation Award Honorable Mention in 2016.