The memory wall has long been recognized as a critical challenge in high-performance systems, and it has recently become even more significant due to the exponential growth of machine learning model sizes. Meanwhile, recent advancements in interconnect technology, such as Compute Express Link (CXL), enable scalable memory system designs to address the memory capacity wall. Moreover, by offloading data and computation to CXL memory expanders to realize Near-Data Processing (NDP), the memory bandwidth wall can also be effectively mitigated. However, designing such a system should be done carefully, considering various design aspects that can affect the practicality of the solution.
In this talk, I will discuss key considerations and directions for building a practical NDP system architecture, including general-purpose computing, low-latency host communication, standard compliance, and cost-effectiveness. I will then present our recent work on an NDP architecture called Memory-Mapped NDP (M²NDP). M²NDP consists of two components: 1) Memory-Mapped Function (M²func), which enables low-latency host-device communication by addressing the overhead of conventional ring buffer-based task offloading, and 2) Memory-Mapped μthreading (M²μthread), a general-purpose, cost-effective NDP unit architecture that aims to maximize resource utilization by hybridizing CPU and GPU architectures. Finally, I will briefly outline future research directions based on the M²NDP architecture.

Gwangsun Kim is an Assistant Professor in the Department of Computer Science and Engineering at POSTECH. Previously, he was a Senior Research Engineer and Senior Performance Engineer at Arm Inc. He received the B.S. degrees in Electronic and Electrical Engineering and Computer Science and Engineering from POSTECH in 2010, and the M.S. and Ph.D. degrees in Computer Science from KAIST in 2012 and 2016, respectively. He has worked on various areas of computer architecture and systems, including memory systems, parallel architectures, GPU computing, systems for machine learning, near-data processing, networking, deep learning compiler, and simulation methodology. He is particularly interested in designing practical architectures for high-performance and scalable systems.

Despite significant progress in verifying protocols, services that implement distributed protocols , e.g., Chubby or Etcd, can exhibit safety bugs in production deployments. These bugs are often introduced by programmers when converting protocol descriptions into code. In this talk I will describe a new technique we have been developing to identify these bugs at runtime: Runtime Protocol Refinement Checking} (RPRC). RPRC systems observe a deployed service's runtime behavior and notify operators when this behavior evidences a protocol implementation bug, allowing operators to mitigate the bugs impact and developers to fix the bug. We have developed an algorithm for RPRC and implemented it in a system called Ellsberg that targets services that assume the asynchronous or partially synchronous model, and fail-stop failures. We designed Ellsberg so it makes no assumptions about how services are implemented, and requires no additional coordination or communication. We have used Ellsberg with three open source services: Etcd, Zookeeper and Redis Raft.

Aurojit Panda is an assistant professor in the Computer Science department at New York University working on systems and networking. He received his PhD in 2017 from UC Berkeley, where he was advised by Scott Shenker. He has received several awards, including a VMware Early Career Faculty Award, a Google Research Scholar Award, an NSF Career award, best paper awards at EuroSys, SIGCOMM and OSDI, and a EuroSys test of time award.