These are projects that I'm currently leading.
If you are interested in my current work, please feel free to reach me. I will be happy to tell you the details and dive into discussions.
Agent systems that generate new ideas to solve problems.
To be announced soon!
To be published in Aug 2026
The Transparency Myth: Disentangling CXL Data from Ethernet State at Rack-Scale
The Directory Tax: Coherence as a Shared-Resource Problem for Fabric-Attached Memory
This work aims to optimize scheduling tasks in dynamic task graph systems. State-of-the-art task parallel frameworks prioritize parallelism without careful considerations of memory requirements. However, Out-of-Memory can lead to severe consequences that it may halt running tasks. While CPUs can be multiplexed, memory shortage cannot be overcome by systems. Prioritizing both memory and parallelism in dynamic task environment is a difficult challenge. We tackle this problem to better utilize cluster resources.
Current application trends put immense pressure on main memory. Many applications scream for more memory. Ironically, memory is severely underutilized at the data center level. We argue that fine-grained memory sharing among cloud applications can improve memory utilization, throughput, and cost. In this paper, we propose to borrow relevant ideas from peer-to-peer networks to bring elasticity to the rigid memory infrastructure of today’s data centers. Instead of sharing content, peers can share their free memory resources with other peers
Even though it is killed with the death of Intel Optane DC, this study poses an interesting view of using Persistent Memory with a new primitive, transactional file system!
This work aims to match file system behavior to optimal access patterns for PM. I implemented a user space transactional file system that manipulates random and unaligned accesses to PM as aligned and sequential accesses with transaction abstraction. It sits between applications and file systems, accelerates write IO and provide full ACID transaction with repeatable reads.