Saved in:
| Main Authors: | Ge, Hao, Feng, Junda, Huang, Qi, Fu, Fangcheng, Nie, Xiaonan, Zuo, Lei, Lin, Haibin, Cui, Bin, Liu, Xin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.21231 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
by: Jiang, Ziheng, et al.
Published: (2024)
by: Jiang, Ziheng, et al.
Published: (2024)
HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
by: Yan, Ran, et al.
Published: (2024)
by: Yan, Ran, et al.
Published: (2024)
Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
by: Li, Haoyang, et al.
Published: (2024)
by: Li, Haoyang, et al.
Published: (2024)
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024)
by: Nie, Xiaonan, et al.
Published: (2024)
Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
by: He, Guoliang, et al.
Published: (2025)
by: He, Guoliang, et al.
Published: (2025)
InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
by: Wang, Shiju, et al.
Published: (2025)
by: Wang, Shiju, et al.
Published: (2025)
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
by: Zhao, Pinxue, et al.
Published: (2024)
by: Zhao, Pinxue, et al.
Published: (2024)
Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
by: Jiang, Youhe, et al.
Published: (2025)
by: Jiang, Youhe, et al.
Published: (2025)
Improving Automatic Parallel Training via Balanced Memory Workload Optimization
by: Wang, Yujie, et al.
Published: (2023)
by: Wang, Yujie, et al.
Published: (2023)
BOute: Cost-Efficient LLM Serving with Heterogeneous LLMs and GPUs via Multi-Objective Bayesian Optimization
by: Jiang, Youhe, et al.
Published: (2026)
by: Jiang, Youhe, et al.
Published: (2026)
MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training
by: Zhao, Juntao, et al.
Published: (2025)
by: Zhao, Juntao, et al.
Published: (2025)
MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
by: Xue, Chunyu, et al.
Published: (2026)
by: Xue, Chunyu, et al.
Published: (2026)
Unleashing Scalable Context Parallelism for Foundation Models Pre-Training via FCP
by: Zhao, Yilong, et al.
Published: (2026)
by: Zhao, Yilong, et al.
Published: (2026)
Training LLMs with Fault Tolerant HSDP on 100,000 GPUs
by: Salpekar, Omkar, et al.
Published: (2026)
by: Salpekar, Omkar, et al.
Published: (2026)
LobRA: Multi-tenant Fine-tuning over Heterogeneous Data
by: Lin, Sheng, et al.
Published: (2025)
by: Lin, Sheng, et al.
Published: (2025)
Hetu v2: A General and Scalable Deep Learning System with Hierarchical and Heterogeneous Single Program Multiple Data Annotations
by: Li, Haoyang, et al.
Published: (2025)
by: Li, Haoyang, et al.
Published: (2025)
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)
Scaled Block Vecchia Approximation for High-Dimensional Gaussian Process Emulation on GPUs
by: Pan, Qilong, et al.
Published: (2025)
by: Pan, Qilong, et al.
Published: (2025)
AMPED: Accelerating MTTKRP for Billion-Scale Sparse Tensor Decomposition on Multiple GPUs
by: Wijeratne, Sasindu, et al.
Published: (2025)
by: Wijeratne, Sasindu, et al.
Published: (2025)
LuWu: An End-to-End In-Network Out-of-Core Optimizer for 100B-Scale Model-in-Network Data-Parallel Training on Distributed GPUs
by: Sun, Mo, et al.
Published: (2024)
by: Sun, Mo, et al.
Published: (2024)
Straggler Tolerant and Resilient DL Training on Homogeneous GPUs
by: Zhang, Zeyu, et al.
Published: (2025)
by: Zhang, Zeyu, et al.
Published: (2025)
Efficient Multi-round LLM Inference over Disaggregated Serving
by: He, Wenhao, et al.
Published: (2026)
by: He, Wenhao, et al.
Published: (2026)
ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments
by: Jiang, Youhe, et al.
Published: (2025)
by: Jiang, Youhe, et al.
Published: (2025)
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM
by: Xi, Shaoke, et al.
Published: (2026)
by: Xi, Shaoke, et al.
Published: (2026)
Scaling to 32 GPUs on a Novel Composable System Architecture
by: Ihnotic, John
Published: (2024)
by: Ihnotic, John
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)
Parallelizing Large-Scale Tensor Network Contraction on Multiple GPUs
by: Pan, Feng, et al.
Published: (2026)
by: Pan, Feng, et al.
Published: (2026)
H2:Towards Efficient Large-Scale LLM Training on Hyper-Heterogeneous Cluster over 1,000 Chips
by: Tang, Ding, et al.
Published: (2025)
by: Tang, Ding, et al.
Published: (2025)
Hydraulis: Balancing Large Transformer Model Training via Co-designing Parallel Strategies and Data Assignment
by: Li, Haoyang, et al.
Published: (2024)
by: Li, Haoyang, et al.
Published: (2024)
Aladdin: Joint Placement and Scaling for SLO-Aware LLM Serving
by: Nie, Chengyi, et al.
Published: (2024)
by: Nie, Chengyi, et al.
Published: (2024)
RTop-K: Ultra-Fast Row-Wise Top-K Selection for Neural Network Acceleration on GPUs
by: Xie, Xi, et al.
Published: (2024)
by: Xie, Xi, et al.
Published: (2024)
Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra
by: Bellavita, Julian, et al.
Published: (2025)
by: Bellavita, Julian, et al.
Published: (2025)
Spindle: Efficient Distributed Training of Multi-Task Large Models via Wavefront Scheduling
by: Wang, Yujie, et al.
Published: (2024)
by: Wang, Yujie, et al.
Published: (2024)
TridentServe: A Stage-level Serving System for Diffusion Pipelines
by: Xia, Yifei, et al.
Published: (2025)
by: Xia, Yifei, et al.
Published: (2025)
Oases: Efficient Large-Scale Model Training on Commodity Servers via Overlapped and Automated Tensor Model Parallelism
by: Li, Shengwei, et al.
Published: (2023)
by: Li, Shengwei, et al.
Published: (2023)
Optimizing Distributed Training Approaches for Scaling Neural Networks
by: Baligodugula, Vishnu Vardhan, et al.
Published: (2025)
by: Baligodugula, Vishnu Vardhan, et al.
Published: (2025)
MegaScale-MoE: Large-Scale Communication-Efficient Training of Mixture-of-Experts Models in Production
by: Jin, Chao, et al.
Published: (2025)
by: Jin, Chao, 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)
Galvatron: An Automatic Distributed System for Efficient Foundation Model Training
by: Liu, Xinyi, et al.
Published: (2025)
by: Liu, Xinyi, et al.
Published: (2025)
Similar Items
-
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
by: Jiang, Ziheng, et al.
Published: (2024) -
HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
by: Yan, Ran, et al.
Published: (2024) -
Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
by: Li, Haoyang, et al.
Published: (2024) -
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024) -
Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
by: He, Guoliang, et al.
Published: (2025)