Reducing Energy Bloat in Large Model Training
Fuente:
arXiv
Saved in:
| Main Authors: | Chung, Jae-Won, Gu, Yile, Jang, Insu, Meng, Luoxi, Bansal, Nikhil, Chowdhury, Mosharaf |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Training
by: Wu, Ruofan, et al.
Published: (2026)
by: Wu, Ruofan, et al.
Published: (2026)
Addressing Variable Heterogeneity in Distributed Multimodal Training with Entrain
by: Jang, Insu, et al.
Published: (2026)
by: Jang, Insu, et al.
Published: (2026)
Efficient Distributed MLLM Training with Cornstarch
by: Jang, Insu, et al.
Published: (2025)
by: Jang, Insu, et al.
Published: (2025)
Toward Cross-Layer Energy Optimizations in AI Systems
by: Chung, Jae-Won, et al.
Published: (2024)
by: Chung, Jae-Won, et al.
Published: (2024)
Where Do the Joules Go? Diagnosing Inference Energy Consumption
by: Chung, Jae-Won, et al.
Published: (2026)
by: Chung, Jae-Won, et al.
Published: (2026)
Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services
by: Liu, Jiachen, et al.
Published: (2024)
by: Liu, Jiachen, et al.
Published: (2024)
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
by: Chung, Jae-Won, et al.
Published: (2026)
by: Chung, Jae-Won, et al.
Published: (2026)
Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models
by: Chung, Jae-Won, et al.
Published: (2026)
by: Chung, Jae-Won, et al.
Published: (2026)
Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving
by: Ma, Jeff J., et al.
Published: (2025)
by: Ma, Jeff J., et al.
Published: (2025)
Venn: Resource Management for Collaborative Learning Jobs
by: Liu, Jiachen, et al.
Published: (2023)
by: Liu, Jiachen, et al.
Published: (2023)
FedTrans: Efficient Federated Learning via Multi-Model Transformation
by: Zhu, Yuxuan, et al.
Published: (2024)
by: Zhu, Yuxuan, et al.
Published: (2024)
TetriServe: Efficient DiT Serving for Heterogeneous Image Generation
by: Lu, Runyu, et al.
Published: (2025)
by: Lu, Runyu, et al.
Published: (2025)
LIBRA: Enabling Workload-aware Multi-dimensional Network Topology Optimization for Distributed Training of Large AI Models
by: Won, William, et al.
Published: (2021)
by: Won, William, et al.
Published: (2021)
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
by: Won, William, et al.
Published: (2023)
by: Won, William, et al.
Published: (2023)
Pipette: Automatic Fine-grained Large Language Model Training Configurator for Real-World Clusters
by: Yim, Jinkyu, et al.
Published: (2024)
by: Yim, Jinkyu, et al.
Published: (2024)
GraNNDis: Efficient Unified Distributed Training Framework for Deep GNNs on Large Clusters
by: Song, Jaeyong, et al.
Published: (2023)
by: Song, Jaeyong, et al.
Published: (2023)
Optimizing Large Model Training through Overlapped Activation Recomputation
by: Chen, Ping, et al.
Published: (2024)
by: Chen, Ping, et al.
Published: (2024)
Energy-Aware Decentralized Learning with Intermittent Model Training
by: Dhasade, Akash, et al.
Published: (2024)
by: Dhasade, Akash, et al.
Published: (2024)
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models
by: Gu, Yile, et al.
Published: (2025)
by: Gu, Yile, et al.
Published: (2025)
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)
Understanding Stragglers in Large Model Training Using What-if Analysis
by: Lin, Jinkun, et al.
Published: (2025)
by: Lin, Jinkun, 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)
Minder: Faulty Machine Detection for Large-scale Distributed Model Training
by: Deng, Yangtao, et al.
Published: (2024)
by: Deng, Yangtao, et al.
Published: (2024)
Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules
by: Pan, Xinglin, et al.
Published: (2024)
by: Pan, Xinglin, et al.
Published: (2024)
Go With The Flow: Churn-Tolerant Decentralized Training of Large Language Models
by: Blagoev, Nikolay, et al.
Published: (2025)
by: Blagoev, Nikolay, et al.
Published: (2025)
Designing Large Foundation Models for Efficient Training and Inference: A Survey
by: Liu, Dong, et al.
Published: (2024)
by: Liu, Dong, et al.
Published: (2024)
A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exporter
by: Choochotkaew, Sunyanan, et al.
Published: (2024)
by: Choochotkaew, Sunyanan, et al.
Published: (2024)
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
by: Jang, Jaeyeon, et al.
Published: (2024)
by: Jang, Jaeyeon, et al.
Published: (2024)
Dependency Aware Incident Linking in Large Cloud Systems
by: Ghosh, Supriyo, et al.
Published: (2024)
by: Ghosh, Supriyo, 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)
Efficient Long-context Language Model Training by Core Attention Disaggregation
by: Zhuang, Yonghao, et al.
Published: (2025)
by: Zhuang, Yonghao, 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)
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)
Accelerating Large Language Model Training with 4D Parallelism and Memory Consumption Estimator
by: Fujii, Kazuki, et al.
Published: (2024)
by: Fujii, Kazuki, et al.
Published: (2024)
BOOST: BOttleneck-Optimized Scalable Training Framework for Low-Rank Large Language Models
by: Wang, Zhengyang, et al.
Published: (2025)
by: Wang, Zhengyang, et al.
Published: (2025)
The Streaming Batch Model for Efficient and Fault-Tolerant Heterogeneous Execution
by: Luan, Frank Sifei, et al.
Published: (2025)
by: Luan, Frank Sifei, 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)
Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
by: Xue, Chunyu, et al.
Published: (2024)
by: Xue, Chunyu, et al.
Published: (2024)
Similar Items
-
Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Training
by: Wu, Ruofan, et al.
Published: (2026) -
Addressing Variable Heterogeneity in Distributed Multimodal Training with Entrain
by: Jang, Insu, et al.
Published: (2026) -
Efficient Distributed MLLM Training with Cornstarch
by: Jang, Insu, et al.
Published: (2025) -
Toward Cross-Layer Energy Optimizations in AI Systems
by: Chung, Jae-Won, et al.
Published: (2024) -
Where Do the Joules Go? Diagnosing Inference Energy Consumption
by: Chung, Jae-Won, et al.
Published: (2026)