MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
Fuente:
arXiv
Saved in:
| Main Authors: | Sheng, Guangming, Su, Junwei, Huang, Chao, Wu, Chuan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by: Ma, Jeffrey, et al.
Published: (2024)
by: Ma, Jeffrey, et al.
Published: (2024)
Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
by: Jung, Hyunji, et al.
Published: (2026)
by: Jung, Hyunji, et al.
Published: (2026)
MQ-GNN: A Multi-Queue Pipelined Architecture for Scalable and Efficient GNN Training
by: Ullah, Irfan, et al.
Published: (2026)
by: Ullah, Irfan, et al.
Published: (2026)
Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
by: Dutta, Ankita, et al.
Published: (2025)
by: Dutta, Ankita, et al.
Published: (2025)
Distributed Deep Learning using Stochastic Gradient Staleness
by: Pham, Viet Hoang, et al.
Published: (2025)
by: Pham, Viet Hoang, et al.
Published: (2025)
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme
by: Park, Jeongmin Brian, et al.
Published: (2024)
by: Park, Jeongmin Brian, et al.
Published: (2024)
MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs
by: Sarkar, Aishwarya, et al.
Published: (2024)
by: Sarkar, Aishwarya, et al.
Published: (2024)
Unleashing Efficient Asynchronous RL Post-Training via Staleness-Constrained Rollout Coordination
by: Li, Haoyang, et al.
Published: (2026)
by: Li, Haoyang, et al.
Published: (2026)
TiMePReSt: Time and Memory Efficient Pipeline Parallel DNN Training with Removed Staleness
by: Dutta, Ankita, et al.
Published: (2024)
by: Dutta, Ankita, et al.
Published: (2024)
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)
by: Wang, Haoming, et al.
Published: (2023)
SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
by: Huang, Xin, et al.
Published: (2024)
by: Huang, Xin, et al.
Published: (2024)
A Tabular Schedule Abstraction for Communication-Aware Evaluation of Pipeline-Parallel LLM Training
by: Barley, Daniel, et al.
Published: (2026)
by: Barley, Daniel, et al.
Published: (2026)
SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learning
by: Su, Jianchang, et al.
Published: (2026)
by: Su, Jianchang, et al.
Published: (2026)
A-3PO: Accelerating Asynchronous LLM Training with Staleness-aware Proximal Policy Approximation
by: Li, Xiaocan, et al.
Published: (2025)
by: Li, Xiaocan, et al.
Published: (2025)
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
by: Yuan, Hao, et al.
Published: (2023)
by: Yuan, Hao, et al.
Published: (2023)
Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024)
by: Yu, Siyuan, et al.
Published: (2024)
NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
by: Jiang, Zhida, et al.
Published: (2026)
by: Jiang, Zhida, et al.
Published: (2026)
AMDP: Asynchronous Multi-Directional Pipeline Parallelism for Large-Scale Models Training
by: Chen, Ling, et al.
Published: (2026)
by: Chen, Ling, et al.
Published: (2026)
HelixPipe: Efficient Distributed Training of Long Sequence Transformers with Attention Parallel Pipeline Parallelism
by: Zhang, Geng, et al.
Published: (2025)
by: Zhang, Geng, et al.
Published: (2025)
Reducing Memory Contention and I/O Congestion for Disk-based GNN Training
by: Jiang, Qisheng, et al.
Published: (2024)
by: Jiang, Qisheng, et al.
Published: (2024)
Heta: Distributed Training of Heterogeneous Graph Neural Networks
by: Zhong, Yuchen, et al.
Published: (2024)
by: Zhong, Yuchen, et al.
Published: (2024)
Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping
by: Jiang, Chenyu, et al.
Published: (2024)
by: Jiang, Chenyu, et al.
Published: (2024)
DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
by: Jiang, Chenyu, et al.
Published: (2025)
by: Jiang, Chenyu, et al.
Published: (2025)
SWIFT: Expedited Failure Recovery for Large-scale DNN Training
by: Zhong, Yuchen, et al.
Published: (2023)
by: Zhong, Yuchen, et al.
Published: (2023)
HybridFlow: A Flexible and Efficient RLHF Framework
by: Sheng, Guangming, et al.
Published: (2024)
by: Sheng, Guangming, et al.
Published: (2024)
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
by: Tian, Ye, et al.
Published: (2024)
by: Tian, Ye, et al.
Published: (2024)
FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
by: Bai, Yuxuan, et al.
Published: (2025)
by: Bai, Yuxuan, et al.
Published: (2025)
ReCycle: Resilient Training of Large DNNs using Pipeline Adaptation
by: Gandhi, Swapnil, et al.
Published: (2024)
by: Gandhi, Swapnil, et al.
Published: (2024)
PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2024)
by: Arfeen, Daiyaan, et al.
Published: (2024)
A Readiness-Driven Runtime for Pipeline-Parallel Training under Runtime Variability
by: Liu, Ruitao, et al.
Published: (2026)
by: Liu, Ruitao, et al.
Published: (2026)
SkipPipe: Partial and Reordered Pipelining Framework for Training LLMs in Heterogeneous Networks
by: Blagoev, Nikolay, et al.
Published: (2025)
by: Blagoev, Nikolay, et al.
Published: (2025)
ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism
by: Liu, Zedong, et al.
Published: (2025)
by: Liu, Zedong, et al.
Published: (2025)
Drift-Aware Federated Learning: A Causal Perspective
by: Fang, Yunjie, et al.
Published: (2025)
by: Fang, Yunjie, et al.
Published: (2025)
An Efficient Gradient-Aware Error-Bounded Lossy Compressor for Federated Learning
by: Ye, Zhijing, et al.
Published: (2025)
by: Ye, Zhijing, et al.
Published: (2025)
TawPipe: Topology-Aware Weight Pipeline Parallelism for Accelerating Long-Context Large Models Training
by: Wu, Houming, et al.
Published: (2025)
by: Wu, Houming, et al.
Published: (2025)
MorphServe: Efficient and Workload-Aware LLM Serving via Runtime Quantized Layer Swapping and KV Cache Resizing
by: Su, Zhaoyuan, et al.
Published: (2025)
by: Su, Zhaoyuan, et al.
Published: (2025)
Single-GPU GNN Systems: Traps and Pitfalls
by: Gong, Yidong, et al.
Published: (2024)
by: Gong, Yidong, et al.
Published: (2024)
QSync: Quantization-Minimized Synchronous Distributed Training Across Hybrid Devices
by: Zhao, Juntao, et al.
Published: (2024)
by: Zhao, Juntao, et al.
Published: (2024)
InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update
by: Wu, Dan, et al.
Published: (2023)
by: Wu, Dan, et al.
Published: (2023)
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
by: Zhan, Ziwei, et al.
Published: (2024)
by: Zhan, Ziwei, et al.
Published: (2024)
Similar Items
-
FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
by: Ma, Jeffrey, et al.
Published: (2024) -
Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation
by: Jung, Hyunji, et al.
Published: (2026) -
MQ-GNN: A Multi-Queue Pipelined Architecture for Scalable and Efficient GNN Training
by: Ullah, Irfan, et al.
Published: (2026) -
Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
by: Dutta, Ankita, et al.
Published: (2025) -
Distributed Deep Learning using Stochastic Gradient Staleness
by: Pham, Viet Hoang, et al.
Published: (2025)