HedraRAG: Co-Optimizing Generation and Retrieval for Heterogeneous RAG Workflows
Year: 2025
Type: conference-paper
Abstract: In this paper, we identify and tackle emerging system-level challenges in serving heterogeneous RAG workflows, characterized by complex stages and diverse request patterns. We present HedraRAG, a new system built on RAGraph, a graph-based abstraction that exposes optimization opportunities across stage-level parallelism, intra-request similarity, and inter-request skewness. These opportunities are expressed through graph transformations, including node splitting, reordering, edge addition and rewiring. Transformations are dynamically applied to wavefronts of subgraphs across concurrent requests and scheduled onto the CPU-GPU pipeline. Experiments across a wide range of workflows demonstrate that HedraRAG achieves more that 1.5× and up to 5× speedup over existing frameworks, offering a comprehensive solution for heterogeneous RAG workload serving.
Authors: Zhengding Hu, Vibha Murthy, Zaifeng Pan, Wanlu Li, Xiaoyi Fang, Yufei Ding, Yuke Wang
Institutions: University of California San Diego, Rice University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W7084138116