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

酷 壳 – CoolShell
酷 壳 – CoolShell
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Tailwind CSS Blog
有赞技术团队
有赞技术团队
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
J
Java Code Geeks
雷峰网
雷峰网
WordPress大学
WordPress大学
L
LangChain Blog
D
DataBreaches.Net
The GitHub Blog
The GitHub Blog
博客园 - 三生石上(FineUI控件)
Microsoft Security Blog
Microsoft Security Blog
P
Proofpoint News Feed
腾讯CDC
GbyAI
GbyAI
罗磊的独立博客
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
F
Fortinet All Blogs
Y
Y Combinator Blog
V
V2EX
A
About on SuperTechFans

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
DHash: Enabling Dynamic and Efficient Hash Tables
Junchang Wang, Xiong Fu, Fu Xiao, Chen Tian · 2020-06-01 · via cs.DC updates on arXiv.org

Given a specified average load factor, hash tables offer the appeal of constant time lookup operations. However, hash tables could face severe hash collisions because of malicious attacks, buggy applications, or even bursts of incoming data, compromising this practical advantage. In this paper, we present DHash, a hash table that overcomes this challenge by allowing programmers to dynamically change its hash function on the fly, without affecting other concurrent operations such as lookup, insert, and delete. DHash is modular and allows programmers to select a variety of lock-free/wait-free set algorithms as the implementation of hash table buckets. With this flexibility, they can make trade-offs between the algorithm's progress guarantee, performance, and engineering efforts, and create DHash implementations that meet their requirements best. Evaluations on three types of architectures show that DHash noticeably outperforms other practical alternatives under heavy workloads. With a load factor of 20, DHash outperforms the other three most widely used hash tables by factors of 1.4-2.0, and when the load factor increases to 200, DHash is 2.3-6.2 times faster.