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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1,621 to 1,680 of 1,783

Model Extraction Warning in MLaaS Paradigm
Manish Kesarwani, Bhaskar Mukhoty, Vijay Arya +1
cs.LGcs.CRcs.DCarXiv:1711.07221v12017
Sparse Communication for Distributed Gradient Descent
Alham Fikri Aji, Kenneth Heafield
cs.CLcs.DCcs.LGarXiv:1704.05021v22017
Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
Weixun Wang, Shaopan Xiong, Gengru Chen +38
cs.LGcs.DCarXiv:2506.06122v12025
Next Generation Cloud Computing: New Trends and Research Directions
Blesson Varghese, Rajkumar Buyya
cs.DCarXiv:1707.07452v32017
Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions
Hadi Amini, Md Jueal Mia, Yasaman Saadati +6
cs.CLcs.CVcs.DCarXiv:2503.16585v12025
Blockchain Technology as a Regulatory Technology: From Code is Law to Law is Code
Primavera De Filippi, Samer Hassan
cs.CYcs.DCarXiv:1801.02507v12018
SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark
Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne +3
cs.DCarXiv:1709.08920v12017
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
Surat Teerapittayanon, Bradley McDanel, H. T. Kung
cs.CVcs.DCarXiv:1709.01921v12017
A Comprehensive Benchmarking Analysis of Fault Recovery in Stream Processing Frameworks
Adriano Vogel, Sören Henning, Esteban Perez-Wohlfeil +2
cs.DCcs.SEarXiv:2404.06203v32024
GraphR: Accelerating Graph Processing Using ReRAM
Linghao Song, Youwei Zhuo, Xuehai Qian +2
cs.DCcs.ARarXiv:1708.06248v42017
FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments
Paola G. Vinueza Naranjo, Zahra Pooranian, Mohammad Shojafar +2
cs.DCcs.NIarXiv:1710.01801v12017
A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster
K. V. Rashmi, Nihar B. Shah, Dikang Gu +3
cs.NIcs.DCcs.ITarXiv:1309.0186v12013
Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey
Mahbuba Afrin, Jiong Jin, Akhlaqur Rahman +3
cs.ROcs.DCarXiv:2104.14270v12021
The blockchain: a new framework for robotic swarm systems
Eduardo Castelló Ferrer
cs.ROcs.DCcs.ETarXiv:1608.00695v42016
DLAU: A Scalable Deep Learning Accelerator Unit on FPGA
Chao Wang, Qi Yu, Lei Gong +3
cs.LGcs.DCcs.NEarXiv:1605.06894v12016
Migrating to Cloud-Native Architectures Using Microservices: An Experience Report
Armin Balalaie, Abbas Heydarnoori, Pooyan Jamshidi
cs.SEcs.DCarXiv:1507.08217v12015
qHiPSTER: The Quantum High Performance Software Testing Environment
Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik
quant-phcs.DCarXiv:1601.07195v22016
Dissecting GPU Memory Hierarchy through Microbenchmarking
Xinxin Mei, Xiaowen Chu
cs.ARcs.DCarXiv:1509.02308v22015
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
Grid: A next generation data parallel C++ QCD library
Peter Boyle, Azusa Yamaguchi, Guido Cossu +1
hep-latcs.DCcs.MSarXiv:1512.03487v12015
Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces
Andrei Costin, Apostolis Zarras, Aurélien Francillon
cs.CRcs.DCcs.NIarXiv:1511.03609v12015
MLlib: Machine Learning in Apache Spark
Xiangrui Meng, Joseph Bradley, Burak Yavuz +13
cs.LGcs.DCcs.MSarXiv:1505.06807v12015
One weird trick for parallelizing convolutional neural networks
Alex Krizhevsky
cs.NEcs.DCcs.LGarXiv:1404.5997v22014
An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-Art
Khalid Alhamazani, Rajiv Ranjan, Karan Mitra +4
cs.DCarXiv:1312.6170v12013
Big Data Computing and Clouds: Trends and Future Directions
Marcos D. Assuncao, Rodrigo N. Calheiros, Silvia Bianchi +2
cs.DCarXiv:1312.4722v22013
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
Luigi Nardi, Bruno Bodin, M. Zeeshan Zia +9
cs.ROcs.CVcs.DCarXiv:1410.2167v22014
Scientific Workflow Applications on Amazon EC2
Gideon Juve, Ewa Deelman, Karan Vahi +4
astro-ph.IMcs.DCarXiv:1005.2718v12010
MDCC: Multi-Data Center Consistency
Tim Kraska, Gene Pang, Michael J. Franklin +1
cs.DBcs.DCarXiv:1203.6049v12012
CloudGenius: Decision Support for Web Server Cloud Migration
Michael Menzel, Rajiv Ranjan
cs.DCcs.SEarXiv:1203.3997v12012
Cloud Migration: A Case Study of Migrating an Enterprise IT System to IaaS
Ali Khajeh-Hosseini, David Greenwood, Ian Sommerville
cs.DCarXiv:1002.3492v12010
Cloudbus Toolkit for Market-Oriented Cloud Computing
Rajkumar Buyya, Suraj Pandey, Christian Vecchiola
cs.DCarXiv:0910.1974v12009
Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities
Rajkumar Buyya, Rajiv Ranjan, Rodrigo N. Calheiros
cs.DCcs.NIarXiv:0907.4878v12009
CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services
Rodrigo N. Calheiros, Rajiv Ranjan, Cesar A. F. De Rose +1
cs.DCcs.OScs.SEarXiv:0903.2525v12009
Fast k Nearest Neighbor Search using GPU
Vincent Garcia, Eric Debreuve, Michel Barlaud
cs.CVcs.DCarXiv:0804.1448v12008
The KV Cache Working Set: Online Capacity Planning for LLM Inference Systems
Luchang Li, Shuaishuai Wang, Zhao Ruan +2
cs.DCarXiv:2609.27746v12026
Reasonable Scale Machine Learning with Open-Source Metaflow
Jacopo Tagliabue, Hugo Bowne-Anderson, Ville Tuulos +3
cs.LGcs.DCcs.SEarXiv:2303.11761v12023
CerebroSim: Scalable Whole-Brain Simulator at 100-Trillion-Synapse Scale on the LineShine Supercomputer
Guangnan Feng, Tianxiang Lyu, Hao Huang +4
cs.DCarXiv:2609.27482v12026
When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse
Yiyu Liu, Minlan Yu, Juncheng Yang
cs.DCcs.LGarXiv:2609.28870v12026
KREX: Concurrent Kernel Benchmarking on Shared GPUs via Region-Granular Exclusivity
Tianyu Feng, Haoxuan Yu, Tianyuan Wu +8
cs.DCcs.OScs.PFarXiv:2609.30057v12026
Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning
Ting Xu, Henry Leung
cs.LGcs.DCarXiv:2609.11937v12026
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla +18
cs.DCcs.LGarXiv:1801.08058v22018
The Internet of Collaborating Things: Agentic Edge AI for Autonomous Cross-Domain Collaboration
Walid A. Hanafy, Nader Sehatbakhsh, David Irwin +2
cs.DCarXiv:2609.10542v12026
Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling
Mobina Kashaniyan, Ali Jannesari
cs.LGcs.DCcs.PFarXiv:2609.19499v22026
Intelligent Transportation Systems Using External Infrastructure: A Literature Survey
Christian Creß, Zhenshan Bing, Alois C. Knoll
cs.ROcs.DCarXiv:2112.05615v42021
LLMs or Naive Bayes? Old Gems or New Ways
Mohammad Firas Sada, Dmitry Mishin, John Graham +3
cs.LGcs.AIcs.CLarXiv:2609.13185v12026
FlowTT: Exploiting Computation Flow Reuse in Irregular Tensor-Train Embedding
Jongmin Seok, Chae Eun Rhee
cs.DCcs.ARarXiv:2609.03459v12026
Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving
Yifan Sui, Han Zhao, Rui Ma +6
cs.DCcs.AIarXiv:2603.18897v32026
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
Youngeun Kwon, Yunjae Lee, Minsoo Rhu
cs.LGcs.ARcs.DCarXiv:1908.03072v22019
DGL-KE: Training Knowledge Graph Embeddings at Scale
Da Zheng, Xiang Song, Chao Ma +6
cs.DCarXiv:2004.08532v12020
A Review on the Application of Blockchain for the Next Generation of Cybersecure Industry 4.0 Smart Factories
Tiago M. Fernandez-Carames, Paula Fraga-Lamas
cs.DCcs.CRcs.CYarXiv:1902.09604v22019
Distributed Inference Performance Optimization for LLMs on CPUs
Pujiang He, Shan Zhou, Changqing Li +5
cs.DCarXiv:2407.00029v12024
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training
Thomas Paine, Hailin Jin, Jianchao Yang +2
cs.CVcs.DCcs.LGarXiv:1312.6186v12013
CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android
Seyyed Salar Latifi Oskouei, Hossein Golestani, Matin Hashemi +1
cs.DCcs.CVarXiv:1511.07376v22015
A Study of Blockchain Oracles
Abdeljalil Beniiche
cs.CRcs.DCarXiv:2004.07140v22020
MARCO: Multi-Agent Code Optimization with Real-Time Knowledge Integration for High-Performance Computing
Asif Rahman, Veljko Cvetkovic, Kathleen Reece +7
cs.DCcs.LGcs.SEarXiv:2505.03906v32025
Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks
Songge Zhang, Wen Wu, Penghui Hu +2
cs.DCarXiv:2305.18889v12023
Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training
Saptadeep Pal, Eiman Ebrahimi, Arslan Zulfiqar +5
cs.LGcs.AIcs.DCarXiv:1907.13257v12019
LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs
Erik Schultheis, Dan Alistarh
cs.DCcs.LGarXiv:2512.15306v12025
One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training
Erik Schultheis, Maximilian Kleinegger, Dan Alistarh
cs.PFcs.DCcs.LGarXiv:2609.11936v12026
Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge
Luca Crupi, Lorenzo Lamberti, Alessandro Giusti +1
cs.ARcs.CVcs.DCarXiv:2609.11939v12026

Distributed and Cluster Computing

Model Extraction Warning in MLaaS Paradigm

Sparse Communication for Distributed Gradient Descent

Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library

Next Generation Cloud Computing: New Trends and Research Directions

Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions

Blockchain Technology as a Regulatory Technology: From Code is Law to Law is Code

SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark

Distributed Deep Neural Networks over the Cloud, the Edge and End Devices

A Comprehensive Benchmarking Analysis of Fault Recovery in Stream Processing Frameworks

GraphR: Accelerating Graph Processing Using ReRAM

FOCAN: A Fog-supported Smart City Network Architecture for Management of Applications in the Internet of Everything Environments

A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

The blockchain: a new framework for robotic swarm systems

DLAU: A Scalable Deep Learning Accelerator Unit on FPGA

Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

qHiPSTER: The Quantum High Performance Software Testing Environment

Dissecting GPU Memory Hierarchy through Microbenchmarking

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

Grid: A next generation data parallel C++ QCD library

Automated Dynamic Firmware Analysis at Scale: A Case Study on Embedded Web Interfaces

MLlib: Machine Learning in Apache Spark

One weird trick for parallelizing convolutional neural networks

An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design Issues, and State-of-the-Art

Big Data Computing and Clouds: Trends and Future Directions

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Scientific Workflow Applications on Amazon EC2

MDCC: Multi-Data Center Consistency

CloudGenius: Decision Support for Web Server Cloud Migration

Cloud Migration: A Case Study of Migrating an Enterprise IT System to IaaS

Cloudbus Toolkit for Market-Oriented Cloud Computing

Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities

CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services

Fast k Nearest Neighbor Search using GPU

The KV Cache Working Set: Online Capacity Planning for LLM Inference Systems

Reasonable Scale Machine Learning with Open-Source Metaflow

CerebroSim: Scalable Whole-Brain Simulator at 100-Trillion-Synapse Scale on the LineShine Supercomputer

When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse

KREX: Concurrent Kernel Benchmarking on Shared GPUs via Region-Granular Exclusivity

Fed-Equilibrium Framework for Topological Pareto Control in Robust and Fair Clinical Federated Learning

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

The Internet of Collaborating Things: Agentic Edge AI for Autonomous Cross-Domain Collaboration

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

Intelligent Transportation Systems Using External Infrastructure: A Literature Survey

LLMs or Naive Bayes? Old Gems or New Ways

FlowTT: Exploiting Computation Flow Reuse in Irregular Tensor-Train Embedding

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

TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning

DGL-KE: Training Knowledge Graph Embeddings at Scale

A Review on the Application of Blockchain for the Next Generation of Cybersecure Industry 4.0 Smart Factories

Distributed Inference Performance Optimization for LLMs on CPUs

GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training

CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android

A Study of Blockchain Oracles

MARCO: Multi-Agent Code Optimization with Real-Time Knowledge Integration for High-Performance Computing

Split Federated Learning: Speed up Model Training in Resource-Limited Wireless Networks

Optimizing Multi-GPU Parallelization Strategies for Deep Learning Training

LLMQ: Efficient Lower-Precision Pretraining for Consumer GPUs

One Simple Trick for Improving the Performance of Energy-Limited Local Inference and Training

Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge