Parallelizing non-linear sequential models over the sequence length
Fuente:
arXiv
Saved in:
| Main Authors: | Lim, Yi Heng, Zhu, Qi, Selfridge, Joshua, Kasim, Muhammad Firmansyah |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
by: Polisetty, Sandeep, et al.
Published: (2023)
by: Polisetty, Sandeep, et al.
Published: (2023)
Age-of-Gradient Updates for Federated Learning over Random Access Channels
by: Wu, Yu Heng, et al.
Published: (2024)
by: Wu, Yu Heng, et al.
Published: (2024)
Effectiveness of Distributed Gradient Descent with Local Steps for Overparameterized Models
by: Zhu, Heng, et al.
Published: (2024)
by: Zhu, Heng, et al.
Published: (2024)
Pipeline Parallelism with Controllable Memory
by: Qi, Penghui, et al.
Published: (2024)
by: Qi, Penghui, et al.
Published: (2024)
On Optimizing the Communication of Model Parallelism
by: Zhuang, Yonghao, et al.
Published: (2022)
by: Zhuang, Yonghao, et al.
Published: (2022)
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)
DynaTrain: Fast Online Parallelism Switching for Elastic LLM Training
by: Wang, Yuanqing, et al.
Published: (2026)
by: Wang, Yuanqing, et al.
Published: (2026)
Edge-Parallel Graph Encoder Embedding
by: Lubonja, Ariel, et al.
Published: (2024)
by: Lubonja, Ariel, et al.
Published: (2024)
TASP: Topology-aware Sequence Parallelism
by: Wang, Yida, et al.
Published: (2025)
by: Wang, Yida, et al.
Published: (2025)
FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism
by: Wang, Yujie, et al.
Published: (2024)
by: Wang, Yujie, et al.
Published: (2024)
Two-dimensional Sparse Parallelism for Large Scale Deep Learning Recommendation Model Training
by: Zhang, Xin, et al.
Published: (2025)
by: Zhang, Xin, et al.
Published: (2025)
MP-SL: Multihop Parallel Split Learning
by: Tirana, Joana, et al.
Published: (2024)
by: Tirana, Joana, et al.
Published: (2024)
Nesterov Method for Asynchronous Pipeline Parallel Optimization
by: Ajanthan, Thalaiyasingam, et al.
Published: (2025)
by: Ajanthan, Thalaiyasingam, et al.
Published: (2025)
MoE Parallel Folding: Heterogeneous Parallelism Mappings for Efficient Large-Scale MoE Model Training with Megatron Core
by: Liu, Dennis, et al.
Published: (2025)
by: Liu, Dennis, et al.
Published: (2025)
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
by: Pan, Qiying, et al.
Published: (2023)
by: Pan, Qiying, et al.
Published: (2023)
MegaScale-Infer: Serving Mixture-of-Experts at Scale with Disaggregated Expert Parallelism
by: Zhu, Ruidong, et al.
Published: (2025)
by: Zhu, Ruidong, et al.
Published: (2025)
Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine
by: Chen, Canyi, et al.
Published: (2025)
by: Chen, Canyi, et al.
Published: (2025)
Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
by: Zhao, Yi, et al.
Published: (2026)
by: Zhao, Yi, et al.
Published: (2026)
ShardTensor: Domain Parallelism for Scientific Machine Learning
by: Adams, Corey, et al.
Published: (2026)
by: Adams, Corey, et al.
Published: (2026)
PiPar: Pipeline Parallelism for Collaborative Machine Learning
by: Zhang, Zihan, et al.
Published: (2022)
by: Zhang, Zihan, et al.
Published: (2022)
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
by: Zhao, Xuanlei, et al.
Published: (2024)
by: Zhao, Xuanlei, et al.
Published: (2024)
PaSE: Parallelization Strategies for Efficient DNN Training
by: Elango, Venmugil
Published: (2024)
by: Elango, Venmugil
Published: (2024)
Heterogeneous Parallelism for Multimodal Large Language Model Training
by: Karnati, Yashaswi, et al.
Published: (2026)
by: Karnati, Yashaswi, et al.
Published: (2026)
Efficient Parallelization Layouts for Large-Scale Distributed Model Training
by: Hagemann, Johannes, et al.
Published: (2023)
by: Hagemann, Johannes, et al.
Published: (2023)
Efficient Parallel Reinforcement Learning Framework using the Reactor Model
by: Kwok, Jacky, et al.
Published: (2023)
by: Kwok, Jacky, et al.
Published: (2023)
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture
by: Zhu, Zehan, et al.
Published: (2023)
by: Zhu, Zehan, et al.
Published: (2023)
DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
by: Niu, Yifan, et al.
Published: (2026)
by: Niu, Yifan, et al.
Published: (2026)
Parallelization of the K-Means Algorithm with Applications to Big Data Clustering
by: Srivastava, Ashish, et al.
Published: (2024)
by: Srivastava, Ashish, et al.
Published: (2024)
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
by: Dhasade, Akash, et al.
Published: (2024)
by: Dhasade, Akash, et al.
Published: (2024)
Graph Neural Networks as Ordering Heuristics for Parallel Graph Coloring
by: Langedal, Kenneth, et al.
Published: (2024)
by: Langedal, Kenneth, et al.
Published: (2024)
Scaling State-Space Models on Multiple GPUs with Tensor Parallelism
by: Dutt, Anurag, et al.
Published: (2026)
by: Dutt, Anurag, et al.
Published: (2026)
AsyncMesh: Fully Asynchronous Optimization for Data and Pipeline Parallelism
by: Ajanthan, Thalaiyasingam, et al.
Published: (2026)
by: Ajanthan, Thalaiyasingam, et al.
Published: (2026)
Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference
by: Prabhakar, Rohan Baskar, et al.
Published: (2024)
by: Prabhakar, Rohan Baskar, et al.
Published: (2024)
Untied Ulysses: Memory-Efficient Context Parallelism via Headwise Chunking
by: Ghadia, Ravi, et al.
Published: (2026)
by: Ghadia, Ravi, et al.
Published: (2026)
ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels
by: Sul, Stuart H., et al.
Published: (2025)
by: Sul, Stuart H., et al.
Published: (2025)
Memory and Bandwidth are All You Need for Fully Sharded Data Parallel
by: Wang, Jiangtao, et al.
Published: (2025)
by: Wang, Jiangtao, 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)
Parallel Track Transformers: Enabling Fast GPU Inference with Reduced Synchronization
by: Wang, Chong, et al.
Published: (2026)
by: Wang, Chong, et al.
Published: (2026)
ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
by: Wu, Zihou, et al.
Published: (2025)
by: Wu, Zihou, et al.
Published: (2025)
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach
by: Fudala, Timo, et al.
Published: (2025)
by: Fudala, Timo, et al.
Published: (2025)
Similar Items
-
GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism
by: Polisetty, Sandeep, et al.
Published: (2023) -
Age-of-Gradient Updates for Federated Learning over Random Access Channels
by: Wu, Yu Heng, et al.
Published: (2024) -
Effectiveness of Distributed Gradient Descent with Local Steps for Overparameterized Models
by: Zhu, Heng, et al.
Published: (2024) -
Pipeline Parallelism with Controllable Memory
by: Qi, Penghui, et al.
Published: (2024) -
On Optimizing the Communication of Model Parallelism
by: Zhuang, Yonghao, et al.
Published: (2022)