32 citations · 44 across the 14 of their papers we have counts for
4 papers · 1 filter
AI Infrastructure in Space: How Far Can We Go?
Qing Li, Qiyang Zhang, Daliang Xu +10
Satellites are becoming programmable computing platforms capable of running increasingly demanding AI workloads. This shift raises a systems problem: how can AI services remain dep…
ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
Bangjun Xiao, Yihao Zhao, Xiangwei Deng +9
Agentic reinforcement learning (RL) has emerged as a transformative workload in cloud clusters, enabling large language models (LLMs) to solve complex problems through interactions…
SeaLLM: Service-Aware and Latency-Optimized Resource Sharing for Large Language Model Inference
Yihao Zhao, Jiadun Chen, Peng Sun +3
Large language models (LLMs) with different architectures and sizes have been developed. Serving each LLM with dedicated GPUs leads to resource waste and service inefficiency due t…
MuxFlow: Efficient and Safe GPU Sharing in Large-Scale Production Deep Learning Clusters
Yihao Zhao, Xin Liu, Shufan Liu +5
Large-scale GPU clusters are widely-used to speed up both latency-critical (online) and best-effort (offline) deep learning (DL) workloads. However, most DL clusters either dedicat…