Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
Fuente:
arXiv
Saved in:
| Main Authors: | Xue, Chunyu, Cui, Weihao, Chen, Quan, Chen, Chen, Zhao, Han, Zhang, Shulai, Wang, Linmei, Li, Yan, Xiao, Limin, Zhang, Weifeng, Yang, Jing, He, Bingsheng, Guo, Minyi |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MuxTune: Efficient Multi-Task LLM Fine-Tuning in Multi-Tenant Datacenters via Spatial-Temporal Backbone Multiplexing
by: Xue, Chunyu, et al.
Published: (2026)
by: Xue, Chunyu, et al.
Published: (2026)
Towards Fast Setup and High Throughput of GPU Serverless Computing
by: Zhao, Han, et al.
Published: (2024)
by: Zhao, Han, et al.
Published: (2024)
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)
SageSched: Efficient LLM Scheduling Confronting Demand Uncertainty and Hybridity
by: Gan, Zhenghao, et al.
Published: (2026)
by: Gan, Zhenghao, et al.
Published: (2026)
S-HPLB: Efficient LLM Attention Serving via Sparsity-Aware Head Parallelism Load Balance
by: Liu, Di, et al.
Published: (2026)
by: Liu, Di, et al.
Published: (2026)
Accelerating Sparse DNNs Based on Tiled GEMM
by: Guo, Cong, et al.
Published: (2024)
by: Guo, Cong, et al.
Published: (2024)
Mosaic: Towards Efficient Training of Multimodal Models with Spatial Resource Multiplexing
by: Wang, Yanbo, et al.
Published: (2026)
by: Wang, Yanbo, et al.
Published: (2026)
AB-Sparse: Sparse Attention with Adaptive Block Size for Accurate and Efficient Long-Context Inference
by: Liu, Di, et al.
Published: (2026)
by: Liu, Di, et al.
Published: (2026)
SPPO:Efficient Long-sequence LLM Training via Adaptive Sequence Pipeline Parallel Offloading
by: Chen, Qiaoling, et al.
Published: (2025)
by: Chen, Qiaoling, et al.
Published: (2025)
Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training
by: Zhang, Han, et al.
Published: (2026)
by: Zhang, Han, et al.
Published: (2026)
Kairos: Low-latency Multi-Agent Serving with Shared LLMs and Excessive Loads in the Public Cloud
by: Chen, Jinyuan, et al.
Published: (2025)
by: Chen, Jinyuan, et al.
Published: (2025)
Towards Resource-Efficient Serverless LLM Inference with SLINFER
by: Xu, Chuhao, et al.
Published: (2025)
by: Xu, Chuhao, et al.
Published: (2025)
LoongTrain: Efficient Training of Long-Sequence LLMs with Head-Context Parallelism
by: Gu, Diandian, et al.
Published: (2024)
by: Gu, Diandian, et al.
Published: (2024)
DASH: Deterministic Attention Scheduling for High-throughput Reproducible LLM Training
by: Qiang, Xinwei, et al.
Published: (2026)
by: Qiang, Xinwei, et al.
Published: (2026)
InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
by: Chen, Hongyu, et al.
Published: (2026)
by: Chen, Hongyu, et al.
Published: (2026)
CFP: Efficient Optimization of Intra-Operator Parallelism Plans for Large Model Training
by: Hu, Weifang, et al.
Published: (2025)
by: Hu, Weifang, et al.
Published: (2025)
Hecate: Unlocking Efficient Sparse Model Training via Fully Sharded Sparse Data Parallelism
by: Qing, Yuhao, et al.
Published: (2025)
by: Qing, Yuhao, et al.
Published: (2025)
InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding
by: Chen, Qiaoling, et al.
Published: (2024)
by: Chen, Qiaoling, et al.
Published: (2024)
Efficient Task Graph Scheduling for Parallel QR Factorization in SLSQP
by: Chatterjee, Soumyajit, et al.
Published: (2025)
by: Chatterjee, Soumyajit, et al.
Published: (2025)
Justitia: Fair and Efficient Scheduling of Task-parallel LLM Agents with Selective Pampering
by: Yang, Mingyan, et al.
Published: (2025)
by: Yang, Mingyan, et al.
Published: (2025)
Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing
by: Wang, Yuxin, et al.
Published: (2023)
by: Wang, Yuxin, et al.
Published: (2023)
Hyperion: Hierarchical Scheduling for Parallel LLM Acceleration in Multi-tier Networks
by: Ma, Mulei, et al.
Published: (2025)
by: Ma, Mulei, et al.
Published: (2025)
Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
by: Li, Yan, et al.
Published: (2025)
by: Li, Yan, et al.
Published: (2025)
Towards Energy Efficient Co-Scheduling in HPC
by: Zheng, Zhong, et al.
Published: (2026)
by: Zheng, Zhong, et al.
Published: (2026)
Schedule-Level Shared-Prefix Reuse for LLM RL Training
by: Li, Pengbo, et al.
Published: (2026)
by: Li, Pengbo, et al.
Published: (2026)
LLM-CoOpt: A Co-Design and Optimization Framework for Efficient LLM Inference on Heterogeneous Platforms
by: Kong, Jie, et al.
Published: (2026)
by: Kong, Jie, et al.
Published: (2026)
CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
by: Chen, Tiancheng, et al.
Published: (2025)
by: Chen, Tiancheng, et al.
Published: (2025)
Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
by: Wang, Tianze, et al.
Published: (2025)
by: Wang, Tianze, et al.
Published: (2025)
FlowWalker: A Memory-efficient and High-performance GPU-based Dynamic Graph Random Walk Framework
by: Mei, Junyi, et al.
Published: (2024)
by: Mei, Junyi, et al.
Published: (2024)
Arrow: Adaptive Scheduling Mechanisms for Disaggregated LLM Inference Architecture
by: Wu, Yu, et al.
Published: (2025)
by: Wu, Yu, et al.
Published: (2025)
Communication-Efficient Serving for Video Diffusion Models with Latent Parallelism
by: Wu, Zhiyuan, et al.
Published: (2025)
by: Wu, Zhiyuan, et al.
Published: (2025)
FREESH: Fair, Resource- and Energy-Efficient Scheduling for LLM Serving on Heterogeneous GPUs
by: He, Xuan, et al.
Published: (2025)
by: He, Xuan, et al.
Published: (2025)
Enhancing Memory Efficiency in Large Language Model Training Through Chronos-aware Pipeline Parallelism
by: Lin, Xinyuan, et al.
Published: (2025)
by: Lin, Xinyuan, et al.
Published: (2025)
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)
Learning from the Past: Adaptive Parallelism Tuning for Stream Processing Systems
by: Han, Yuxing, et al.
Published: (2025)
by: Han, Yuxing, et al.
Published: (2025)
DreamDDP: Accelerating Data Parallel Distributed LLM Training with Layer-wise Scheduled Partial Synchronization
by: Tang, Zhenheng, et al.
Published: (2025)
by: Tang, Zhenheng, et al.
Published: (2025)
GMLake: Efficient and Transparent GPU Memory Defragmentation for Large-scale DNN Training with Virtual Memory Stitching
by: Guo, Cong, et al.
Published: (2024)
by: Guo, Cong, et al.
Published: (2024)
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)
HARP: Orchestrating Automated Parallel Training on Heterogeneous GPU Clusters
by: Liang, Antian, et al.
Published: (2025)
by: Liang, Antian, et al.
Published: (2025)
NasZip: Software and Hardware Co-Design to Accelerate Approximate Nearest Neighbor Search with DIMM-Based Near-Data Processing
by: Zou, Cheng, et al.
Published: (2026)
by: Zou, Cheng, et al.
Published: (2026)
Similar Items
-
MuxTune: Efficient Multi-Task LLM Fine-Tuning in Multi-Tenant Datacenters via Spatial-Temporal Backbone Multiplexing
by: Xue, Chunyu, et al.
Published: (2026) -
Towards Fast Setup and High Throughput of GPU Serverless Computing
by: Zhao, Han, et al.
Published: (2024) -
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) -
SageSched: Efficient LLM Scheduling Confronting Demand Uncertainty and Hybridity
by: Gan, Zhenghao, et al.
Published: (2026) -
S-HPLB: Efficient LLM Attention Serving via Sparsity-Aware Head Parallelism Load Balance
by: Liu, Di, et al.
Published: (2026)