Cover Image

COPUS: Co-adaptive Parallelism and Batch Size Selection in Large Language Model Training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sakip, Akhmed, Fuadi, Erland Hilman, Sayedelahl, Omar, Li, Zonghang, She, Jianshu, Aji, Alham Fikri, Liu, Steve, Xing, Eric, Ho, Qirong
Format:	Preprint
Published:	2026
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LAPS: A Length-Aware-Prefill LLM Serving System
by: She, Jianshu, et al.
Published: (2026)

Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation
by: Mansurov, Jonibek, et al.
Published: (2024)

Predicting the Order of Upcoming Tokens Improves Language Modeling
by: Zuhri, Zayd M. K., et al.
Published: (2025)

Softpick: No Attention Sink, No Massive Activations with Rectified Softmax
by: Zuhri, Zayd M. K., et al.
Published: (2025)

Token Level Routing Inference System for Edge Devices
by: She, Jianshu, et al.
Published: (2025)

Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
by: Li, Zonghang, et al.
Published: (2025)

On Optimizing the Communication of Model Parallelism
by: Zhuang, Yonghao, et al.
Published: (2022)

LinguDistill: Recovering Linguistic Ability in Vision-Language Models via Selective Cross-Modal Distillation
by: Irawan, Patrick Amadeus, et al.
Published: (2026)

Herring: Parallel Batch-Order-Fairness on DAG-based Blockchain Consensus
by: Putnik, Marko, et al.
Published: (2026)

Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training
by: Feng, Wenjiao, et al.
Published: (2025)

Joint Optimization of Offloading, Batching and DVFS for Multiuser Co-Inference
by: Xu, Yaodan, et al.
Published: (2025)

Collaborative Batch Size Optimization for Federated Learning
by: Geimer, Arno, et al.
Published: (2025)

On Optimal Batch Size in Coded Computing
by: Saha, Swapnil, et al.
Published: (2025)

Parallel Batch-Dynamic Maximal Independent Set
by: Blelloch, Guy, et al.
Published: (2026)

Parallel Point-to-Point Shortest Paths and Batch Queries
by: Dong, Xiaojun, et al.
Published: (2025)

Constraint Programming Models For Serial Batch Scheduling With Minimum Batch Size
by: Huertas, Jorge A., et al.
Published: (2025)

COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances
by: Wibowo, Haryo Akbarianto, et al.
Published: (2023)

Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
by: Barrak, Amine, et al.
Published: (2025)

HarmonyBatch: Batching multi-SLO DNN Inference with Heterogeneous Serverless Functions
by: Chen, Jiabin, et al.
Published: (2024)

Parallel $k$-Core Decomposition with Batched Updates and Asynchronous Reads
by: Liu, Quanquan C., et al.
Published: (2024)

DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
by: Shovan, S M, et al.
Published: (2026)

Energy-efficient Federated Learning with Dynamic Model Size Allocation
by: Kumar, M S Chaitanya, et al.
Published: (2024)

Faster Parallel Batch-Dynamic Algorithms for Low Out-Degree Orientation
by: Blelloch, Guy, et al.
Published: (2026)

Parallel Batch-Dynamic Maximal Matching with Constant Work per Update
by: Blelloch, Guy E., et al.
Published: (2025)

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
by: Dai, Yuanjun, et al.
Published: (2025)

A Parallel CPU-GPU Framework for Batching Heuristic Operations in Depth-First Heuristic Search
by: Futuhi, Ehsan, et al.
Published: (2025)

CoCoDiff: Optimizing Collective Communications for Distributed Diffusion Transformer Inference Under Ulysses Sequence Parallelism
by: Ma, Bin, et al.
Published: (2026)

CoCoI: Distributed Coded Inference System for Straggler Mitigation
by: Liu, Xing, et al.
Published: (2025)

Parallel $k$d-tree with Batch Updates
by: Men, Ziyang, et al.
Published: (2024)

GPU-Accelerated Batch-Dynamic Subgraph Matching
by: Qiu, Linshan, et al.
Published: (2024)

CPMA: An Efficient Batch-Parallel Compressed Set Without Pointers
by: Wheatman, Brian, et al.
Published: (2023)

FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
by: Lin, Shouxu, et al.
Published: (2025)

Enabling Large Batch Size Training for DNN Models Beyond the Memory Limit While Maintaining Performance
by: Piao, XinYu, et al.
Published: (2021)

Parallel Batch Dynamic Vertex Coloring in $O(\log Δ)$ Amortized Update Time
by: Hutton, Chase, et al.
Published: (2025)

Balancing Pipeline Parallelism with Vocabulary Parallelism
by: Yeung, Man Tsung, et al.
Published: (2024)

Coded Distributed (Batch) Matrix Multiplication over Galois Ring via RMFE
by: Kuang, Yi, et al.
Published: (2024)

StatuScale: Status-aware and Elastic Scaling Strategy for Microservice Applications
by: Wen, Linfeng, et al.
Published: (2024)

Are Your Epochs Too Epic? Batch Free Can Be Harmful
by: Kim, Daewoo, et al.
Published: (2024)

Batched DGEMMs for scientific codes running on long vector architectures
by: Banchelli, Fabio, et al.
Published: (2025)

Batch Denoising for AIGC Service Provisioning in Wireless Edge Networks
by: Xu, Jinghang, et al.
Published: (2025)