2 citations · 3 across the 11 of their papers we have counts for
12 papers
RegionCache: Semantic-Aware Region Reuse for Efficient Multi-Turn Image Generation
Peizheng Li, Xin Ai, Hanyuan Liu +2
Real-world image generation often involves multi-turn editing, where users iteratively modify small regions while most image content remains unchanged. However, existing diffusion…
DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management
Linfang Chen, Zhen Song, Lei Liu +6
Memory-based Temporal Graph Neural Networks (M-TGNNs) maintain recursively updated node states to capture fine-grained temporal interactions. However, existing distributed framewor…
NeutronSparse: Coordinating Heterogeneous Engines for Sparse Matrix Multiplication on NPUs
Xin Ai, Zeyu Ling, Hao Yuan +4
Sparse matrix-matrix multiplication (SpMM) is a fundamental data operation for large-scale sparse data processing. With NPUs increasingly deployed in data centers for their perform…
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…
EvoRAG: Making Knowledge Graph-based RAG Automatically Evolve through Feedback Backpropagation
Zhenbo Fu, Yuanzhe Zhang, Qiange Wang +5
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) has emerged as a promising paradigm for enhancing LLM reasoning by retrieving multi-hop paths from KGs. However, exist…
Incremental GNN Embedding Computation on Streaming Graphs
Qiange Wang, Haoran Lv, Yanfeng Zhang +2
Graph Neural Network (GNN) on streaming graphs has gained increasing popularity. However, its practical deployment remains challenging, as the inference process relies on Runtime E…
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)…
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…
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…
Towards Transaction as a Service
Yanfeng Zhang, Weixing Zhou, Yang Ren +3
This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a tr…
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…