2 citations · 5 across the 10 of their papers we have counts for
10 papers
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…
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…
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…
HyTGraph: GPU-Accelerated Graph Processing with Hybrid Transfer Management
Qiange Wang, Xin Ai, Yanfeng Zhang +2
Processing large graphs with memory-limited GPU needs to resolve issues of host-GPU data transfer, which is a key performance bottleneck. Existing GPU-accelerated graph processing…
i2MapReduce: Incremental MapReduce for Mining Evolving Big Data
Yanfeng Zhang, Shimin Chen, Qiang Wang +1
As new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refr…