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

WordPress大学
WordPress大学
L
LangChain Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
罗磊的独立博客
J
Java Code Geeks
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 叶小钗
小众软件
小众软件
博客园 - Franky
D
Docker
Google DeepMind News
Google DeepMind News
Microsoft Azure Blog
Microsoft Azure Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
U
Unit 42
宝玉的分享
宝玉的分享
C
Check Point Blog
B
Blog
V
V2EX
博客园 - 三生石上(FineUI控件)
MyScale Blog
MyScale Blog
The Cloudflare Blog
博客园 - 聂微东
博客园_首页
Engineering at Meta
Engineering at Meta

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 monitoring tool for a GRID operation center
S. Andreozzi, S. Fantinel, D. Rebatto, L. Vaccarossa, G. Tortone · 2003-06-04 · via cs.DC updates on arXiv.org

WorldGRID is an intercontinental testbed spanning Europe and the US integrating architecturally different Grid implementations based on the Globus toolkit. The WorldGRID testbed has been successfully demonstrated during the WorldGRID demos at SuperComputing 2002 (Baltimore) and IST2002 (Copenhagen) where real HEP application jobs were transparently submitted from US and Europe using "native" mechanisms and run where resources were available, independently of their location. To monitor the behavior and performance of such testbed and spot problems as soon as they arise, DataTAG has developed the EDT-Monitor tool based on the Nagios package that allows for Virtual Organization centric views of the Grid through dynamic geographical maps. The tool has been used to spot several problems during the WorldGRID operations, such as malfunctioning Resource Brokers or Information Servers, sites not correctly configured, job dispatching problems, etc. In this paper we give an overview of the package, its features and scalability solutions and we report on the experience acquired and the benefit that a GRID operation center would gain from such a tool.