Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
Fuente:
arXiv
Guardado en:
| Autores principales: | Xu, Chongyang, Siebenbrunner, Christoph, Bindschaedler, Laurent |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
por: Merkel, Nikolai, et al.
Publicado: (2026)
por: Merkel, Nikolai, et al.
Publicado: (2026)
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
por: Olshevskyi, Rostyslav, et al.
Publicado: (2024)
por: Olshevskyi, Rostyslav, et al.
Publicado: (2024)
ReInc: Scaling Training of Dynamic Graph Neural Networks
por: Guan, Mingyu, et al.
Publicado: (2025)
por: Guan, Mingyu, et al.
Publicado: (2025)
CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
por: Zhang, Shuai, et al.
Publicado: (2024)
por: Zhang, Shuai, et al.
Publicado: (2024)
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
por: Polisetty, Sandeep, et al.
Publicado: (2023)
por: Polisetty, Sandeep, et al.
Publicado: (2023)
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
por: Merkel, Nikolai, et al.
Publicado: (2023)
por: Merkel, Nikolai, et al.
Publicado: (2023)
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch
por: Bajaj, Saurabh, et al.
Publicado: (2024)
por: Bajaj, Saurabh, et al.
Publicado: (2024)
Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
por: Waleffe, Roger, et al.
Publicado: (2025)
por: Waleffe, Roger, et al.
Publicado: (2025)
SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
por: Huang, Xin, et al.
Publicado: (2024)
por: Huang, Xin, et al.
Publicado: (2024)
Distributed Matrix-Based Sampling for Graph Neural Network Training
por: Tripathy, Alok, et al.
Publicado: (2023)
por: Tripathy, Alok, et al.
Publicado: (2023)
Graph Neural Networks Gone Hogwild
por: Solodova, Olga, et al.
Publicado: (2024)
por: Solodova, Olga, et al.
Publicado: (2024)
Cooperative Minibatching in Graph Neural Networks
por: Balin, Muhammed Fatih, et al.
Publicado: (2023)
por: Balin, Muhammed Fatih, et al.
Publicado: (2023)
Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring
por: Langedal, Kenneth, et al.
Publicado: (2024)
por: Langedal, Kenneth, et al.
Publicado: (2024)
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters
por: Rama, Pranav, et al.
Publicado: (2024)
por: Rama, Pranav, et al.
Publicado: (2024)
Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
por: Jiang, Chenyu, et al.
Publicado: (2024)
por: Jiang, Chenyu, et al.
Publicado: (2024)
Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
por: Barwey, Shivam, et al.
Publicado: (2024)
por: Barwey, Shivam, et al.
Publicado: (2024)
BLoad: Enhancing Neural Network Training with Efficient Sequential Data Handling
por: Ruschel, Raphael, et al.
Publicado: (2023)
por: Ruschel, Raphael, et al.
Publicado: (2023)
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
por: Yoon, Daegun, et al.
Publicado: (2024)
por: Yoon, Daegun, et al.
Publicado: (2024)
Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
por: Naman, Pranjal, et al.
Publicado: (2025)
por: Naman, Pranjal, et al.
Publicado: (2025)
Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
por: Yan, Xueming, et al.
Publicado: (2024)
por: Yan, Xueming, et al.
Publicado: (2024)
Graph Neural Networks and Reinforcement Learning for Proactive Application Image Placement
por: Makris, Antonios, et al.
Publicado: (2024)
por: Makris, Antonios, et al.
Publicado: (2024)
Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
por: Lu, Yishun, et al.
Publicado: (2026)
por: Lu, Yishun, et al.
Publicado: (2026)
Scalable and Adaptive Parallel Training of Graph Transformer on Large Graphs
por: Lin, Jun-Liang, et al.
Publicado: (2026)
por: Lin, Jun-Liang, et al.
Publicado: (2026)
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
por: Chen, Zehuan, et al.
Publicado: (2025)
por: Chen, Zehuan, et al.
Publicado: (2025)
Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
por: Deng, Yangtao, et al.
Publicado: (2025)
por: Deng, Yangtao, et al.
Publicado: (2025)
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
por: Hu, Tianhao, et al.
Publicado: (2026)
por: Hu, Tianhao, et al.
Publicado: (2026)
GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU
por: Yu, Zhongming, et al.
Publicado: (2024)
por: Yu, Zhongming, et al.
Publicado: (2024)
AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
por: Bai, Huawei, et al.
Publicado: (2025)
por: Bai, Huawei, et al.
Publicado: (2025)
BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models
por: Wang, Zhengyang, et al.
Publicado: (2025)
por: Wang, Zhengyang, et al.
Publicado: (2025)
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
por: Lu, Fred, et al.
Publicado: (2024)
por: Lu, Fred, et al.
Publicado: (2024)
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
por: Jeon, Byungsoo, et al.
Publicado: (2024)
por: Jeon, Byungsoo, et al.
Publicado: (2024)
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
por: Chen, Yuanyuan, et al.
Publicado: (2022)
por: Chen, Yuanyuan, et al.
Publicado: (2022)
Mask-Encoded Sparsification: Mitigating Biased Gradients in Communication-Efficient Split Learning
por: Zhou, Wenxuan, et al.
Publicado: (2024)
por: Zhou, Wenxuan, et al.
Publicado: (2024)
Communication Optimization for Distributed Training: Architecture, Advances, and Opportunities
por: Wei, Yunze, et al.
Publicado: (2024)
por: Wei, Yunze, et al.
Publicado: (2024)
MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training
por: Yoon, Daegun, et al.
Publicado: (2023)
por: Yoon, Daegun, et al.
Publicado: (2023)
Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
por: Kralj, Ivan, et al.
Publicado: (2024)
por: Kralj, Ivan, et al.
Publicado: (2024)
Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture
por: Xu, Zuyu, et al.
Publicado: (2025)
por: Xu, Zuyu, et al.
Publicado: (2025)
SparDL: Distributed Deep Learning Training with Efficient Sparse Communication
por: Zhao, Minjun, et al.
Publicado: (2023)
por: Zhao, Minjun, et al.
Publicado: (2023)
Leiden-Fusion Partitioning Method for Effective Distributed Training of Graph Embeddings
por: Bai, Yuhe, et al.
Publicado: (2024)
por: Bai, Yuhe, et al.
Publicado: (2024)
Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
por: Wang, Shuai, et al.
Publicado: (2025)
por: Wang, Shuai, et al.
Publicado: (2025)
Ejemplares similares
-
EmbedPart: Embedding-Driven Graph Partitioning for Scalable Graph Neural Network Training
por: Merkel, Nikolai, et al.
Publicado: (2026) -
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
por: Olshevskyi, Rostyslav, et al.
Publicado: (2024) -
ReInc: Scaling Training of Dynamic Graph Neural Networks
por: Guan, Mingyu, et al.
Publicado: (2025) -
CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
por: Zhang, Shuai, et al.
Publicado: (2024) -
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
por: Polisetty, Sandeep, et al.
Publicado: (2023)