:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Xu, Chongyang, Siebenbrunner, Christoph, Bindschaedler, Laurent
Format:	Preprint
Publié:	2026
Sujets:	Distributed, Parallel, and Cluster Computing Machine Learning
Accès en ligne:	https://arxiv.org/abs/2602.01872
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

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

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

ReInc: Scaling Training of Dynamic Graph Neural Networks
par: Guan, Mingyu, et autres
Publié: (2025)

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

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

An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
par: Merkel, Nikolai, 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)

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

Distributed Matrix-Based Sampling for Graph Neural Network Training
par: Tripathy, Alok, 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)

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

Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
par: Barwey, Shivam, et autres
Publié: (2024)

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

Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
par: Jiang, Chenyu, et autres
Publié: (2024)

Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
par: Lin, Jun-Liang, et autres
Publié: (2026)

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

Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows
par: Mohammadi, Bardia, et autres
Publié: (2026)

Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
par: Yoon, Daegun, et autres
Publié: (2024)

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)

Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
par: Lu, Yishun, et autres
Publié: (2026)

GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
par: Jeon, Byungsoo, et autres
Publié: (2024)

Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
par: Deng, Yangtao, et autres
Publié: (2025)

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

DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
par: Hu, Tianhao, et autres
Publié: (2026)

Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
par: Kralj, Ivan, 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)

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
par: Lu, Fred, et autres
Publié: (2024)

AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
par: Bai, Huawei, et autres
Publié: (2025)

BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models
par: Wang, Zhengyang, et autres
Publié: (2025)

Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning
par: Zhou, Wenxuan, et autres
Publié: (2024)

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

Communication Optimization for Distributed Training: Architecture, Advances, and Opportunities
par: Wei, Yunze, et autres
Publié: (2024)

MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
par: Yoon, Daegun, et autres
Publié: (2023)

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression
par: Condat, Laurent, et autres
Publié: (2024)

Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture
par: Xu, Zuyu, et autres
Publié: (2025)

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
par: Zhao, Minjun, et autres
Publié: (2023)