惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

人人都是产品经理
人人都是产品经理
Google DeepMind News
Google DeepMind News
博客园 - 【当耐特】
量子位
博客园 - 司徒正美
爱范儿
爱范儿
Hugging Face - Blog
Hugging Face - Blog
博客园 - 聂微东
Jina AI
Jina AI
J
Java Code Geeks
腾讯CDC
大猫的无限游戏
大猫的无限游戏
V
Visual Studio Blog
I
InfoQ
D
Docker
Recent Announcements
Recent Announcements
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
宝玉的分享
宝玉的分享
G
Google Developers Blog
GbyAI
GbyAI
Y
Y Combinator Blog
有赞技术团队
有赞技术团队
H
Help Net Security

cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
An Application-Level Dependable Technique for Farmer-Work...
Vincenzo De Florio, Geert Deconinck, Rudy Lauwereins · 2016-01-11 · via cs.DC updates on arXiv.org

An application-level technique is described for farmer-worker parallel applications which allows a worker to be added or removed from the computing farm at any moment of the run time without affecting the overall outcome of the computation. The technique is based on uncoupling the farmer from the workers by means of a separate module which asynchronously feeds these latter with new "units of work" on an on-demand basis, and on a special feeding strategy based on bookkeeping the status of each work-unit. An augmentation of the LINDA model is finally proposed to exploit the bookkeeping algorithm for tuple management.