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

Hugging Face - Blog
Hugging Face - Blog
云风的 BLOG
云风的 BLOG
Google DeepMind News
Google DeepMind News
美团技术团队
J
Java Code Geeks
V
V2EX
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
The Cloudflare Blog
宝玉的分享
宝玉的分享
博客园 - Franky
Y
Y Combinator Blog
爱范儿
爱范儿
H
Help Net Security
腾讯CDC
G
Google Developers Blog
B
Blog RSS Feed
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
阮一峰的网络日志
阮一峰的网络日志
罗磊的独立博客
V
Visual Studio Blog
The GitHub Blog
The GitHub Blog
博客园_首页
C
Check Point Blog
博客园 - 三生石上(FineUI控件)

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
Fast Parallel I/O on Cluster Computers
Thomas Duessel, Norbert Eicker, Florin Isaila, Thomas Lippert, T · 2003-03-19 · via cs.DC updates on arXiv.org

Today's cluster computers suffer from slow I/O, which slows down I/O-intensive applications. We show that fast disk I/O can be achieved by operating a parallel file system over fast networks such as Myrinet or Gigabit Ethernet. In this paper, we demonstrate how the ParaStation3 communication system helps speed-up the performance of parallel I/O on clusters using the open source parallel virtual file system (PVFS) as testbed and production system. We will describe the set-up of PVFS on the Alpha-Linux-Cluster-Engine (ALiCE) located at Wuppertal University, Germany. Benchmarks on ALiCE achieve write-performances of up to 1 GB/s from a 32-processor compute-partition to a 32-processor PVFS I/O-partition, outperforming known benchmark results for PVFS on the same network by more than a factor of 2. Read-performance from buffer-cache reaches up to 2.2 GB/s. Our benchmarks are giant, I/O-intensive eigenmode problems from lattice quantum chromodynamics, demonstrating stability and performance of PVFS over Parastation in large-scale production runs.