Cover Image

When Less is More: Achieving Faster Convergence in Distributed Edge Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Basani, Advik Raj, Vivek, Siddharth Chaitra, Krishna, Advaith, Paul, Arnab K.
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning Performance
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Less is More: Optimizing Function Calling for LLM Execution on Edge Devices
by: Paramanayakam, Varatheepan, et al.
Published: (2024)

Cost-Performance Evaluation of General Compute Instances: AWS, Azure, GCP, and OCI
by: Tharwani, Jay, et al.
Published: (2024)

Evaluating HPC-Style CPU Performance and Cost in Virtualized Cloud Infrastructures
by: Tharwani, Jay, et al.
Published: (2025)

Bridding OT and PaaS in Edge-to-Cloud Continuum
by: Barrios, Carlos J, et al.
Published: (2025)

On Orchestrating Parallel Broadcasts for Distributed Ledgers
by: Sheng, Peiyao, et al.
Published: (2024)

An Online Probabilistic Distributed Tracing System
by: Toslali, M., et al.
Published: (2024)

WebAssembly and Unikernels: A Comparative Study for Serverless at the Edge
by: Besozzi, Valerio, et al.
Published: (2025)

FalconFS: Distributed File System for Large-Scale Deep Learning Pipeline
by: Xu, Jingwei, et al.
Published: (2025)

Collaborative Processing for Multi-Tenant Inference on Memory-Constrained Edge TPUs
by: Ng, Nathan, et al.
Published: (2026)

DREAMS: Decentralized Resource Allocation and Service Management across the Compute Continuum Using Service Affinity
by: Dinh-Tuan, Hai, et al.
Published: (2025)

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment
by: Vatsavai, Sairam Sri, et al.
Published: (2025)

Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications
by: Scheinert, Dominik, et al.
Published: (2023)

Kubernetes in Action: Exploring the Performance of Kubernetes Distributions in the Cloud
by: Aqasizade, Hossein, et al.
Published: (2024)

Ridgeline: A 2D Roofline Model for Distributed Systems
by: Checconi, Fabio, et al.
Published: (2022)

Cache Blocking of Distributed-Memory Parallel Matrix Power Kernels
by: Lacey, Dane C., et al.
Published: (2024)

KEET: Explaining Performance of GPU Kernels Using LLM Agents
by: Davis, Joshua H., et al.
Published: (2026)

SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication
by: Zhuang, Chen, et al.
Published: (2025)

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study
by: McDonald, Jesse, et al.
Published: (2024)

Towards a Scalable and Efficient PGAS-based Distributed OpenMP
by: Shan, Baodi, et al.
Published: (2024)

RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference
by: Karfakis, George, et al.
Published: (2025)

QoSFlow: Ensuring Service Quality of Distributed Workflows Using Interpretable Sensitivity Models
by: Rashid, Md Hasanur, et al.
Published: (2026)

Modeling the Impact of Fiber Latency on Compute-Communication Overlap in Geo-Distributed Multi-Datacenter AI Training
by: Papavasileiou, Ioannis, et al.
Published: (2026)

A Multi-Port Concurrent Communication Model for handling Compute Intensive Tasks on Distributed Satellite System Constellations
by: Veeravalli, Bharadwaj
Published: (2026)

UnifyFL: Enabling Decentralized Cross-Silo Federated Learning
by: S, Sarang, et al.
Published: (2025)

An Experimental Study of Different Aggregation Schemes in Semi-Asynchronous Federated Learning
by: Li, Yunbo, et al.
Published: (2024)

Learning-Augmented Performance Model for Tensor Product Factorization in High-Order FEM
by: Ren, Xuanzhengbo, et al.
Published: (2026)

DIAL: Decentralized I/O AutoTuning via Learned Client-side Local Metrics for Parallel File System
by: Rashid, Md Hasanur, et al.
Published: (2026)

Pipit: Scripting the analysis of parallel execution traces
by: Bhatele, Abhinav, et al.
Published: (2023)

ABS: Adaptive Bounded Staleness Converges Faster and Communicates Less
by: Tan, Qiao, et al.
Published: (2023)

GPU Cluster Scheduling for Network-Sensitive Deep Learning
by: Sharma, Aakash, et al.
Published: (2024)

Kairos: Efficient Temporal Graph Analytics on a Single Machine
by: da Trindade, Joana M. F., et al.
Published: (2024)

Flowshop Machine Scheduling: Markov Modeling, Optimal Schedules and Heuristics
by: Ghanem, Samah A. M.
Published: (2025)

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
by: Pinnock, Alyssa, et al.
Published: (2025)

IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency
by: Ghafouri, Saeid, et al.
Published: (2023)

cedar: Optimized and Unified Machine Learning Input Data Pipelines
by: Zhao, Mark, et al.
Published: (2024)

Mitigating GIL Bottlenecks in Edge AI Systems
by: Mandal, Mridankan, et al.
Published: (2026)

Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions
by: Zhang, Zongshun, et al.
Published: (2025)

Towards Universal Performance Modeling for Machine Learning Training on Multi-GPU Platforms
by: Lin, Zhongyi, et al.
Published: (2024)

Extracting Practical, Actionable Energy Insights from Supercomputer Telemetry and Logs
by: Cornelius, Melanie, et al.
Published: (2025)

Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers
by: Zhuang, Chen, et al.
Published: (2024)