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

腾讯CDC
Engineering at Meta
Engineering at Meta
Last Week in AI
Last Week in AI
V
Visual Studio Blog
Stack Overflow Blog
Stack Overflow Blog
A
About on SuperTechFans
博客园 - 司徒正美
D
DataBreaches.Net
有赞技术团队
有赞技术团队
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
I
InfoQ
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
月光博客
月光博客
Google DeepMind News
Google DeepMind News
Recent Announcements
Recent Announcements
小众软件
小众软件
G
Google Developers Blog
博客园 - 【当耐特】
U
Unit 42
美团技术团队
B
Blog
D
Docker
Blog — PlanetScale
Blog — PlanetScale

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
Space Bounds for Reliable Multi-Writer Data Store: Inhere...
Gregory Chockler, Dan Dobre, Alexander Shraer, Alexander Spiegel · 2015-08-16 · via cs.DC updates on arXiv.org

Reliable storage emulations from fault-prone components have established themselves as an algorithmic foundation of modern storage services and applications. Most existing reliable storage emulations are built from storage services supporting arbitrary read-modify-write primitives. Since such primitives are not typically exposed by pre-existing or off-the-shelf components (such as cloud storage services or network-attached disks) it is natural to ask if they are indeed essential for efficient storage emulations. In this paper, we answer this question in the affirmative. We show that relaxing the underlying storage to only support read/write operations leads to a linear blow-up in the emulation space requirements. We also show that the space complexity is not adaptive to concurrency, which implies that the storage cannot be reliably reclaimed even in sequential runs. On a positive side, we show that Compare-and-Swap primitives, which are commonly available with many off-the-shelf storage services, can be used to emulate a reliable multi-writer atomic register with constant storage and adaptive time complexity.