Unicron: Economizing Self-Healing LLM Training at Scale
Fuente:
arXiv
Saved in:
| Main Authors: | He, Tao, Li, Xue, Wang, Zhibin, Qian, Kun, Xu, Jingbo, Yu, Wenyuan, Zhou, Jingren |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
by: Chen, Yanxi, et al.
Published: (2023)
by: Chen, Yanxi, et al.
Published: (2023)
SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips
by: Lian, Xinyu, et al.
Published: (2025)
by: Lian, Xinyu, 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)
STAR: Decode-Phase Rescheduling for LLM Inference
by: Wang, Zhibin, et al.
Published: (2025)
by: Wang, Zhibin, et al.
Published: (2025)
Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2025)
by: Arfeen, Daiyaan, et al.
Published: (2025)
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
by: Yao, Yuhang, et al.
Published: (2024)
by: Yao, Yuhang, et al.
Published: (2024)
Echo: Simulating Distributed Training At Scale
by: Feng, Yicheng, et al.
Published: (2024)
by: Feng, Yicheng, et al.
Published: (2024)
History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL
by: He, Jingkai, et al.
Published: (2025)
by: He, Jingkai, et al.
Published: (2025)
Harli: SLO-Aware Co-location of LLM Inference and PEFT-based Finetuning on Model-as-a-Service Platforms
by: Xu, Ao, et al.
Published: (2025)
by: Xu, Ao, 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)
DHP: Efficient Scaling of MLLM Training with Dynamic Hybrid Parallelism
by: Niu, Yifan, et al.
Published: (2026)
by: Niu, Yifan, et al.
Published: (2026)
Mycroft: Tracing Dependencies in Collective Communication Towards Reliable LLM Training
by: Deng, Yangtao, et al.
Published: (2025)
by: Deng, Yangtao, et al.
Published: (2025)
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)
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)
Adacc: An Adaptive Framework Unifying Compression and Activation Recomputation for LLM Training
by: Chen, Ping, et al.
Published: (2025)
by: Chen, Ping, et al.
Published: (2025)
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)
Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation
by: Liang, Yunkai, et al.
Published: (2025)
by: Liang, Yunkai, et al.
Published: (2025)
Photon: Federated LLM Pre-Training
by: Sani, Lorenzo, et al.
Published: (2024)
by: Sani, Lorenzo, et al.
Published: (2024)
Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training
by: Fernandez, Jared, et al.
Published: (2024)
by: Fernandez, Jared, et al.
Published: (2024)
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
by: Xiao, Youshao, et al.
Published: (2024)
by: Xiao, Youshao, et al.
Published: (2024)
Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor with T10
by: Liu, Yiqi, et al.
Published: (2024)
by: Liu, Yiqi, et al.
Published: (2024)
TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
by: Chai, Huichao, et al.
Published: (2026)
by: Chai, Huichao, et al.
Published: (2026)
Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients
by: Li, Yan, et al.
Published: (2024)
by: Li, Yan, et al.
Published: (2024)
Understanding Silent Data Corruption in LLM Training
by: Ma, Jeffrey, et al.
Published: (2025)
by: Ma, Jeffrey, et al.
Published: (2025)
EMO: Edge Model Overlays to Scale Model Size in Federated Learning
by: Wu, Di, et al.
Published: (2025)
by: Wu, Di, et al.
Published: (2025)
AsyncHZP: Hierarchical ZeRO Parallelism with Asynchronous Scheduling for Scalable LLM Training
by: Bai, Huawei, et al.
Published: (2025)
by: Bai, Huawei, et al.
Published: (2025)
The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
by: Chen, Huamin, et al.
Published: (2026)
by: Chen, Huamin, et al.
Published: (2026)
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
by: Jia, Jinda, et al.
Published: (2024)
by: Jia, Jinda, et al.
Published: (2024)
HetCCL: Accelerating LLM Training with Heterogeneous GPUs
by: Kim, Heehoon, et al.
Published: (2026)
by: Kim, Heehoon, et al.
Published: (2026)
SwiftSpec: Ultra-Low Latency LLM Decoding by Scaling Asynchronous Speculative Decoding
by: Zhang, Ziyi, et al.
Published: (2025)
by: Zhang, Ziyi, et al.
Published: (2025)
Efficient Parallelization Layouts for Large-Scale Distributed Model Training
by: Hagemann, Johannes, et al.
Published: (2023)
by: Hagemann, Johannes, 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)
ReInc: Scaling Training of Dynamic Graph Neural Networks
by: Guan, Mingyu, et al.
Published: (2025)
by: Guan, Mingyu, et al.
Published: (2025)
Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training
by: Lu, Yishun, et al.
Published: (2026)
by: Lu, Yishun, et al.
Published: (2026)
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)
DASH: Deterministic Attention Scheduling for High-throughput Reproducible LLM Training
by: Qiang, Xinwei, et al.
Published: (2026)
by: Qiang, Xinwei, et al.
Published: (2026)
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
by: Wu, Zhiyuan, et al.
Published: (2025)
by: Wu, Zhiyuan, et al.
Published: (2025)
ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
by: Xiang, Yuxing, et al.
Published: (2025)
by: Xiang, Yuxing, et al.
Published: (2025)
Federated Temporal Graph Clustering
by: Zhou, Zihao, et al.
Published: (2024)
by: Zhou, Zihao, et al.
Published: (2024)
Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks
by: Waleffe, Roger, et al.
Published: (2025)
by: Waleffe, Roger, et al.
Published: (2025)
Similar Items
-
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
by: Chen, Yanxi, et al.
Published: (2023) -
SuperOffload: Unleashing the Power of Large-Scale LLM Training on Superchips
by: Lian, Xinyu, et al.
Published: (2025) -
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
by: Zhao, Pinxue, et al.
Published: (2024) -
STAR: Decode-Phase Rescheduling for LLM Inference
by: Wang, Zhibin, et al.
Published: (2025) -
Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2025)