activity
20232026
most citedNeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering

Qing Yang, Pengcheng Huang, Xinze Li +6

Long-video question answering remains challenging for Vision-Language Models (VLMs), as answer-relevant evidence is often sparse, transient, and temporally dispersed across lengthy…

cs.DC2024

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…

cs.DC2023★ 2 cited

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…

cs.LG2023

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…

cs.LG2023★ 1 cited

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…