Image de couverture de livre

ReInc: Scaling Training of Dynamic Graph Neural Networks

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Guan, Mingyu, Singhal, Saumia, Kim, Taesoo, Iyer, Anand Padmanabha
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Distributed, Parallel, and Cluster Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection
par: Gupta, Vima, et autres
Publié: (2024)

GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
par: Polisetty, Sandeep, et autres
Publié: (2023)

Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch
par: Bajaj, Saurabh, et autres
Publié: (2024)

Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
par: Waleffe, Roger, et autres
Publié: (2025)

OMEGA: A Low-Latency GNN Serving System for Large Graphs
par: Kim, Geon-Woo, et autres
Publié: (2025)

SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
par: Huang, Xin, et autres
Publié: (2024)

Fully Distributed Online Training of Graph Neural Networks in Networked Systems
par: Olshevskyi, Rostyslav, et autres
Publié: (2024)

Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
par: Xu, Chongyang, et autres
Publié: (2026)

An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
par: Merkel, Nikolai, et autres
Publié: (2023)

eMoE: Task-aware Memory Efficient Mixture-of-Experts-Based (MoE) Model Inference
par: Tairin, Suraiya, et autres
Publié: (2025)

Distributed Matrix-Based Sampling for Graph Neural Network Training
par: Tripathy, Alok, et autres
Publié: (2023)

Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
par: Dai, Yinwei, et autres
Publié: (2023)

Graph Neural Networks Gone Hogwild
par: Solodova, Olga, et autres
Publié: (2024)

Cooperative Minibatching in Graph Neural Networks
par: Balin, Muhammed Fatih, et autres
Publié: (2023)

EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
par: Merkel, Nikolai, et autres
Publié: (2026)

Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring
par: Langedal, Kenneth, et autres
Publié: (2024)

DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
par: Niu, Yifan, et autres
Publié: (2026)

CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
par: Zhang, Shuai, et autres
Publié: (2024)

A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings
par: Bai, Yuhe
Publié: (2025)

Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
par: Rama, Pranav, et autres
Publié: (2024)

BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling
par: Ruschel, Raphael, et autres
Publié: (2023)

Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality
par: de Almeida, Filipe Miguel Gonçalves, et autres
Publié: (2025)

Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
par: Naman, Pranjal, et autres
Publié: (2025)

Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
par: Yan, Xueming, et autres
Publié: (2024)

Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
par: Makris, Antonios, et autres
Publié: (2024)

ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation
par: Gandhi, Swapnil, et autres
Publié: (2024)

Echo: Simulating Distributed Training At Scale
par: Feng, Yicheng, et autres
Publié: (2024)

High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
par: Chen, Zehuan, et autres
Publié: (2025)

ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
par: Won, William, et autres
Publié: (2023)

Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
par: Fernandez, Jared, et autres
Publié: (2024)

GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU
par: Yu, Zhongming, et autres
Publié: (2024)

Greedy Shapley Client Selection for Communication-Efficient Federated Learning
par: Singhal, Pranava, et autres
Publié: (2023)

Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
par: Wu, Xiabao, et autres
Publié: (2025)

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
par: Chen, Yuanyuan, et autres
Publié: (2022)

LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
par: Liu, Xinyi, et autres
Publié: (2026)

Unicron: Economizing Self-Healing LLM Training at Scale
par: He, Tao, et autres
Publié: (2023)

MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
par: Jiang, Ziheng, et autres
Publié: (2024)

Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
par: Kralj, Ivan, et autres
Publié: (2024)

Efficient Parallelization Layouts for Large-Scale Distributed Model Training
par: Hagemann, Johannes, et autres
Publié: (2023)

MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production
par: Jin, Chao, et autres
Publié: (2025)