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

博客园 - Franky
J
Java Code Geeks
腾讯CDC
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Jina AI
Jina AI
博客园 - 司徒正美
Stack Overflow Blog
Stack Overflow Blog
美团技术团队
L
LangChain Blog
WordPress大学
WordPress大学
A
About on SuperTechFans
Martin Fowler
Martin Fowler
月光博客
月光博客
Y
Y Combinator Blog
U
Unit 42
D
Docker
Recent Announcements
Recent Announcements
Hugging Face - Blog
Hugging Face - Blog
B
Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
G
Google Developers Blog
Last Week in AI
Last Week in AI
T
The Blog of Author Tim Ferriss
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com

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
Establishing Applicability of SSDs to LHC Tier-2 Hardware...
2011-02-16 · via cs.DC updates on arXiv.org

Solid State Disk technologies are increasingly replacing high-speed hard disks as the storage technology in high-random-I/O environments. There are several potentially I/O bound services within the typical LHC Tier-2 - in the back-end, with the trend towards many-core architectures continuing, worker nodes running many single-threaded jobs and storage nodes delivering many simultaneous files can both exhibit I/O limited efficiency. We estimate the effectiveness of affordable SSDs in the context of worker nodes, on a large Tier-2 production setup using both low level tools and real LHC I/O intensive data analysis jobs comparing and contrasting with high performance spinning disk based solutions. We consider the applicability of each solution in the context of its price/performance metrics, with an eye on the pragmatic issues facing Tier-2 provision and upgrades