2 citations · 5 across the 14 of their papers we have counts for
15 papers
AcOrch: Accelerating Sampling-based GNN Training under CPU-NPU Heterogeneous Environments
Kefu Chen, Xin Ai, Qiange Wang +2
Graph Neural Networks (GNNs) have achieved remarkable success in various applications. Sampling-based GNN training, which conducts mini-batch training on sampled subgraphs, has bec…
ATCC: Adaptive Concurrency Control for Unforeseen Agentic Transactions
Weixing Zhou, Zhiyou Wang, Zeshun Peng +3
Data agents, empowered by Large Language Models (LLMs), introduce a new paradigm in transaction processing. Unlike traditional applications with fixed patterns, data agents run onl…
Towards Autonomous Graph Data Analytics with Analytics-Augmented Generation
Qiange Wang, Chaoyi Chen, Jingqi Gao +3
This paper argues that reliable end-to-end graph data analytics cannot be achieved by retrieval- or code-generation-centric LLM agents alone. Although large language models (LLMs)…
AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence
Geunbin Yu
As large language models from diverse providers converge toward comparable benchmark performance, the traditional paradigm of selecting a single best model per task yields diminish…
GeoLayer: Towards Low-Latency and Cost-Efficient Geo-Distributed Graph Stores with Layered Graph
Feng Yao, Xiaokang Yang, Shufeng Gong +3
The inherent connectivity and dependency of graph-structured data, combined with its unique topology-driven access patterns, pose fundamental challenges to conventional data replic…
DGAI: Decoupled On-Disk Graph-Based ANN Index for Efficient Updates and Queries
Jiahao Lou, Shufeng Gong, Quan Yu +6
On-disk graph-based indexes are favored for billion-scale Approximate Nearest Neighbor Search (ANNS) due to their high performance and cost-efficiency. However, existing systems ty…
GoVector: An I/O-Efficient Caching Strategy for High-Dimensional Vector Nearest Neighbor Search
Yijie Zhou, Shengyuan Lin, Shufeng Gong +4
Graph-based high-dimensional vector indices have become a mainstream solution for large-scale approximate nearest neighbor search (ANNS). However, their substantial memory footprin…
A Topology-Aware Localized Update Strategy for Graph-Based ANN Index
Song Yu, Shengyuan Lin, Shufeng Gong +7
The graph-based index has been widely adopted to meet the demand for approximate nearest neighbor search (ANNS) for high-dimensional vectors. However, in dynamic scenarios involvin…
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism
Xin Ai, Hao Yuan, Zeyu Ling +6
Graph neural networks (GNNs) have emerged as a promising direction. Training large-scale graphs that relies on distributed computing power poses new challenges. Existing distribute…
TRAPP: An Efficient Point-to-Point Path Planning Algorithm for Road Networks with Restrictions
Hanzhang Chen, Xiangzhi Zhang, Shufeng Gong +4
Path planning is a fundamental problem in road networks, with the goal of finding a path that optimizes objectives such as shortest distance or minimal travel time. Existing method…
LSMGraph: A High-Performance Dynamic Graph Storage System with Multi-Level CSR
Song Yu, Shufeng Gong, Qian Tao +9
The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, e…
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments
Xin Ai, Qiange Wang, Chunyu Cao +5
Graph Neural Networks (GNNs) have demonstrated outstanding performance in various applications. Existing frameworks utilize CPU-GPU heterogeneous environments to train GNN models a…
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
Chaoyi Chen, Dechao Gao, Yanfeng Zhang +6
Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks as…
GastCoCo: Graph Storage and Coroutine-Based Prefetch Co-Design for Dynamic Graph Processing
Hongfu Li, Qian Tao, Song Yu +6
An efficient data structure is fundamental to meeting the growing demands in dynamic graph processing. However, the dual requirements for graph computation efficiency (with contigu…
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
Hao Yuan, Yajiong Liu, Yanfeng Zhang +5
Many Graph Neural Network (GNN) training systems have emerged recently to support efficient GNN training. Since GNNs embody complex data dependencies between training samples, the…