most citedQ-adaptive: A Multi-Agent Reinforcement Learning Based Routing on Dragonfly Network

25 citations · 65 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC202413 cited

Union: An Automatic Workload Manager for Accelerating Network Simulation

Xin Wang, Misbah Mubarak, Yao Kang +2

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machin…

cs.NI202425 cited

Q-adaptive: A Multi-Agent Reinforcement Learning Based Routing on Dragonfly Network

Yao Kang, Xin Wang, Zhiling Lan

High-radix interconnects such as Dragonfly and its variants rely on adaptive routing to balance network traffic for optimum performance. Ideally, adaptive routing attempts to forwa…

cs.DC202415 cited

MRSch: Multi-Resource Scheduling for HPC

Boyang Li, Yuping Fan, Matthew Dearing +4

Emerging workloads in high-performance computing (HPC) are embracing significant changes, such as having diverse resource requirements instead of being CPU-centric. This advancemen…

cs.NI20247 cited

Study of Workload Interference with Intelligent Routing on Dragonfly

Yao Kang, Xin Wang, Zhiling Lan

Dragonfly interconnect is a crucial network technology for supercomputers. To support exascale systems, network resources are shared such that links and routers are not dedicated t…

cs.LG20245 cited

Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling

Boyang Li, Zhiling Lan, Michael E. Papka

In the field of high-performance computing (HPC), there has been recent exploration into the use of deep reinforcement learning for cluster scheduling (DRL scheduling), which has d…