2 citations · 2 across the 5 of their papers we have counts for
5 papers
MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems
Yifei Wang, Hancheng Ye, Yechen Xu +8
Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic workloads induce a temporal shi…
Nixie: Efficient, Transparent Temporal Multiplexing for Consumer GPUs
Yechen Xu, Yifei Wang, Nathanael Ren +2
Consumer machines are increasingly running large ML workloads such as large language models (LLMs), text-to-image generation, and interactive image editing. Unlike datacenter GPUs,…
RLAX: Large-Scale, Distributed Reinforcement Learning for Large Language Models on TPUs
Runlong Zhou, Lefan Zhang, Shang-Chen Wu +29
Reinforcement learning (RL) has emerged as the de-facto paradigm for improving the reasoning capabilities of large language models (LLMs). We have developed RLAX, a scalable RL fra…
Conveyor: Efficient Tool-aware LLM Serving with Tool Partial Execution
Yechen Xu, Xinhao Kong, Tingjun Chen +1
The complexity of large language model (LLM) serving workloads has substantially increased due to the integration with external tool invocations, such as ChatGPT plugins. In this p…
Remote Procedure Call as a Managed System Service
Jingrong Chen, Yongji Wu, Shihan Lin +6
Remote Procedure Call (RPC) is a widely used abstraction for cloud computing. The programmer specifies type information for each remote procedure, and a compiler generates stub cod…