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

S
SegmentFault 最新的问题
MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
博客园 - Franky
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
V
Visual Studio Blog
IT之家
IT之家
美团技术团队
博客园 - 司徒正美
Martin Fowler
Martin Fowler
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
雷峰网
雷峰网
B
Blog
WordPress大学
WordPress大学
aimingoo的专栏
aimingoo的专栏
D
Docker
P
Proofpoint News Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
爱范儿
爱范儿

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
A Java Based Architecture of P2P-Grid Middleware
B. Hudzia, T. N. Ellahi, L. McDermott, T. Kechadi · 2006-08-29 · via cs.DC updates on arXiv.org

During the last decade there has been a huge interest in Grid technologies, and numerous Grid projects have been initiated with various visions of the Grid. While all these visions have the same goal of resource sharing, they differ in the functionality that a Grid supports, characterization, programming environments, etc. We present a new Grid system dedicated to dealing with data issues, called DGET (Data Grid Environment and Tools). DGET is characterized by its peerto- peer communication system and entity-based architecture, therefore, taking advantage of the main functionality of both systems; P2P and Grid. DGET is currently under development and a prototype implementing the main components is in its first phase of testing. In this paper we limit our description to the system architectural features and to the main differences with other systems. Keywords: Grid Computing, Peer to Peer, Peer to Peer Grid