Distributed and Cluster Computing

Papers filed under cs.DC on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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241 to 300 of 1,071

Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs
Yujie Zhang, Huiying Lan, Ehsan Aghapour +5
cs.DCcs.LGcs.PFarXiv:2609.04168v12026
Improving Progressive Compression with Adaptive Interpolation and Coefficient Decomposition
Wenbo Li, Xuan Wu, Qian Gong +6
cs.DCarXiv:2609.04573v12026
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Yae Jee Cho, Luyang Liu, Zheng Xu +2
cs.LGcs.DCarXiv:2401.06432v22024
Collaborative On-Sensor Array Cameras
Jipeng Sun, Kaixuan Wei, Thomas Eboli +6
physics.opticscs.CVcs.DCarXiv:2506.04061v12025
CloudGenius: Decision Support for Web Server Cloud Migration
Michael Menzel, Rajiv Ranjan
cs.DCcs.SEarXiv:1203.3997v12012
Thinking Like a Vertex: a Survey of Vertex-Centric Frameworks for Distributed Graph Processing
Robert Ryan McCune, Tim Weninger, Gregory Madey
cs.DCarXiv:1507.04405v12015
CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
Qiaoling Chen, Zhisheng Ye, Tian Tang +7
cs.DCarXiv:2601.22705v12026
Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics
Xiao Yang, Yinan Ni, Yuqi Tang +3
cs.DCcs.LGarXiv:2601.20389v12026
Modern Computing: Vision and Challenges
Sukhpal Singh Gill, Huaming Wu, Panos Patros +22
cs.DCarXiv:2401.02469v12024
Same Request, Different Answer: Quantization Amplifies Cache-Induced Divergence in LLM Serving
Aditi Patodiya
cs.SEcs.DCcs.LGarXiv:2609.04748v12026
Solution-space heterogeneity shapes federated learning dynamics across partial differential equations
Ping Luo, Jiahuan Wang, Ziqing Wen +2
cs.LGcs.DCarXiv:2609.05012v12026
Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning
Ming Xiang, Stratis Ioannidis, Edmund Yeh +2
cs.LGcs.DCmath.OCarXiv:2609.04763v12026
Optimizing CNN Model Inference on CPUs
Yizhi Liu, Yao Wang, Ruofei Yu +3
cs.DCarXiv:1809.02697v32018
Edge Impulse: An MLOps Platform for Tiny Machine Learning
Shawn Hymel, Colby Banbury, Daniel Situnayake +13
cs.DCcs.LGcs.SEarXiv:2212.03332v32022
Scalable Training of Mixture-of-Experts Models with Megatron Core
Zijie Yan, Hongxiao Bai, Xin Yao +42
cs.DCcs.CLcs.LGarXiv:2603.07685v22026
SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica +1
stat.MLcs.DCcs.LGarXiv:1511.06051v42015
ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios
Xinyi Hu, Yuhao Shen, Baolin Zhang +6
cs.DCcs.AIcs.LGarXiv:2604.09603v22026
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism
Jay H. Park, Gyeongchan Yun, Chang M. Yi +5
cs.DCarXiv:2005.14038v12020
A Unifying Framework for Parallel and Distributed Processing in R using Futures
Henrik Bengtsson
cs.DCstat.COarXiv:2008.00553v42020
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
Jianqing Zhang, Yang Liu, Yang Hua +1
cs.LGcs.CRcs.DCarXiv:2401.03230v12024
Optimal Deterministic Routing and Sorting on the Congested Clique
Christoph Lenzen
cs.DCarXiv:1207.1852v42012
Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
Redowan Mahmud, Kotagiri Ramamohanarao, Rajkumar Buyya
cs.DCeess.SParXiv:2005.10460v12020
Split Learning for collaborative deep learning in healthcare
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang +3
cs.LGcs.DCstat.MLarXiv:1912.12115v12019
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
Suyog Gupta, Wei Zhang, Fei Wang
stat.MLcs.DCcs.LGarXiv:1509.04210v32015
LLM-42: Enabling Determinism in LLM Inference with Verified Speculation
Raja Gond, Aditya K Kamath, Ramachandran Ramjee +1
cs.LGcs.AIcs.DCarXiv:2601.17768v22026
Decision Support Tools for Cloud Migration in the Enterprise
Ali Khajeh-Hosseini, Ian Sommerville, Jurgen Bogaerts +1
cs.DCarXiv:1105.0149v12011
Joint Temporal-Structural Representation Learning for Distributed Fault Discrimination in Microservice Architectures
Yihan Xue, Yuxiao Wang, Ao Zhu +2
cs.DCarXiv:2605.01776v12026
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
Yuke Wang, Boyuan Feng, Gushu Li +4
cs.DCarXiv:2006.06608v32020
Communication-Computation Efficient Gradient Coding
Min Ye, Emmanuel Abbe
stat.MLcs.DCcs.ITarXiv:1802.03475v12018
Reference Architecture of a Quantum-Centric Supercomputer
Seetharami Seelam, Jerry M. Chow, Antonio Córcoles +10
cs.ETcs.ARcs.DCarXiv:2603.10970v22026
Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter
Ruoyu Qin, Weiran He, Yaoyu Wang +5
cs.DCarXiv:2604.15039v22026
First Analysis of Local GD on Heterogeneous Data
Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
cs.LGcs.DCmath.NAarXiv:1909.04715v22019
LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure
Jaehong Cho, Hyunmin Choi, Guseul Heo +1
cs.DCcs.AIarXiv:2602.23036v22026
LightLDA: Big Topic Models on Modest Compute Clusters
Jinhui Yuan, Fei Gao, Qirong Ho +6
stat.MLcs.DCcs.IRarXiv:1412.1576v12014
KernelFoundry: Hardware-aware evolutionary GPU kernel optimization
Nina Wiedemann, Quentin Leboutet, Michael Paulitsch +2
cs.DCcs.LGarXiv:2603.12440v22026
Compute and Storage Clouds Using Wide Area High Performance Networks
Robert L. Grossman, Yunhong Gu, Michael Sabala +1
cs.DCarXiv:0808.1802v12008
AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving
Tianhao Xu, Yiming Liu, Xianglong Lu +18
cs.LGcs.AIcs.DCarXiv:2601.06288v12026
Characterization of Large Language Model Development in the Datacenter
Qinghao Hu, Zhisheng Ye, Zerui Wang +9
cs.DCcs.LGarXiv:2403.07648v22024
Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling
Ahmed Elnaggar, Hazem Essam, Wafaa Salah-Eldin +4
cs.LGcs.CLcs.DCarXiv:2301.06568v12023
Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving
Yifan Sui, Han Zhao, Rui Ma +6
cs.DCcs.AIarXiv:2603.18897v32026
RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz +6
cs.AIcs.DCcs.LGarXiv:1712.09381v42017
Effectively Prefetching Remote Memory with Leap
Hasan Al Maruf, Mosharaf Chowdhury
cs.DCcs.OSarXiv:1911.09829v12019
Jolteon and Ditto: Network-Adaptive Efficient Consensus with Asynchronous Fallback
Rati Gelashvili, Lefteris Kokoris-Kogias, Alberto Sonnino +2
cs.DCcs.CRarXiv:2106.10362v42021
Where Do the Joules Go? Diagnosing Inference Energy Consumption
Jae-Won Chung, Ruofan Wu, Jeff J. Ma +1
cs.LGcs.DCarXiv:2601.22076v22026
PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response
Yueying Li, Jiayang Chen, Yuanfan Chen +5
cs.DCcs.AIarXiv:2608.21719v12026
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning
Qiong Wu, Yu Zhao, Qiang Fan +3
cs.DCcs.LGarXiv:2208.01219v12022
Algorithmic Simplification for Million-Vertex Diffusion History Reconstruction
Gökhan Göktürk
cs.SIcs.DCarXiv:2608.28955v12026
EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles
Shuai Yu, Xiaowen Gong, Qian Shi +2
cs.NIcs.AIcs.DCarXiv:2101.06056v12021
Energy-Aware Load Balancing in Content Delivery Networks
Vimal Mathew, Ramesh K. Sitaraman, Prashant Shenoy
cs.NIcs.DCarXiv:1109.5641v12011
Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference
Pol G. Recasens, Ferran Agullo, Yue Zhu +5
cs.DCcs.LGarXiv:2503.08311v22025
Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows
Bardia Mohammadi, Nearchos Potamitis, Lars Klein +2
cs.LGcs.AIcs.DCarXiv:2602.14849v22026
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
cs.LGcs.DCcs.PFarXiv:2110.01406v32021
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Mohammad Noormohammadpour, Cauligi S. Raghavendra
cs.NIcs.DCcs.PFarXiv:1712.03530v12017
A Unified Coding Framework for Distributed Computing with Straggling Servers
Songze Li, Mohammad Ali Maddah-Ali, A. Salman Avestimehr
cs.ITcs.DCarXiv:1609.01690v12016
TurboTransformers: An Efficient GPU Serving System For Transformer Models
Jiarui Fang, Yang Yu, Chengduo Zhao +1
cs.DCcs.AIcs.LGarXiv:2010.05680v42020
vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models
Peiqi Yin, Jiangyun Zhu, Han Gao +13
cs.DCarXiv:2602.02204v12026
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable
Laxman Dhulipala, Guy E. Blelloch, Julian Shun
cs.DScs.DCarXiv:1805.05208v42018
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads
John Thorpe, Yifan Qiao, Jonathan Eyolfson +8
cs.DCcs.LGarXiv:2105.11118v22021
Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
Roberto Vercellino, Jared Willard, Gustavo Campos +4
eess.SYcs.DCcs.LGarXiv:2604.07345v12026
Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Haoxiang Luo, Yinqiu Liu, Ruichen Zhang +7
cs.NIcs.DCarXiv:2507.00672v12025

Distributed and Cluster Computing

Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs

Improving Progressive Compression with Adaptive Interpolation and Coefficient Decomposition

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models

Collaborative On-Sensor Array Cameras

CloudGenius: Decision Support for Web Server Cloud Migration

Thinking Like a Vertex: a Survey of Vertex-Centric Frameworks for Distributed Graph Processing

CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control

Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics

Modern Computing: Vision and Challenges

Same Request, Different Answer: Quantization Amplifies Cache-Induced Divergence in LLM Serving

Solution-space heterogeneity shapes federated learning dynamics across partial differential equations

Resilience Beyond Stationary Client Unavailability: Unlocking Efficient and Unbiased Federated Learning

Optimizing CNN Model Inference on CPUs

Edge Impulse: An MLOps Platform for Tiny Machine Learning

Scalable Training of Mixture-of-Experts Models with Megatron Core

SparkNet: Training Deep Networks in Spark

ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

A Unifying Framework for Parallel and Distributed Processing in R using Futures

FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning

Optimal Deterministic Routing and Sorting on the Congested Clique

Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions

Split Learning for collaborative deep learning in healthcare

Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study

LLM-42: Enabling Determinism in LLM Inference with Verified Speculation

Decision Support Tools for Cloud Migration in the Enterprise

Joint Temporal-Structural Representation Learning for Distributed Fault Discrimination in Microservice Architectures

GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs

Communication-Computation Efficient Gradient Coding

Reference Architecture of a Quantum-Centric Supercomputer

Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter

First Analysis of Local GD on Heterogeneous Data

LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure

LightLDA: Big Topic Models on Modest Compute Clusters

KernelFoundry: Hardware-aware evolutionary GPU kernel optimization

Compute and Storage Clouds Using Wide Area High Performance Networks

AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving

Characterization of Large Language Model Development in the Datacenter

Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving

RLlib: Abstractions for Distributed Reinforcement Learning

Effectively Prefetching Remote Memory with Leap

Jolteon and Ditto: Network-Adaptive Efficient Consensus with Asynchronous Fallback

Where Do the Joules Go? Diagnosing Inference Energy Consumption

PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Algorithmic Simplification for Million-Vertex Diffusion History Reconstruction

EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles

Energy-Aware Load Balancing in Content Delivery Networks

Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference

Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation

Datacenter Traffic Control: Understanding Techniques and Trade-offs

A Unified Coding Framework for Distributed Computing with Straggling Servers

TurboTransformers: An Efficient GPU Serving System For Transformer Models

vLLM-Omni: Fully Disaggregated Serving for Any-to-Any Multimodal Models

Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads

Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration