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

量子位
博客园_首页
罗磊的独立博客
云风的 BLOG
云风的 BLOG
J
Java Code Geeks
Last Week in AI
Last Week in AI
D
DataBreaches.Net
Jina AI
Jina AI
博客园 - Franky
大猫的无限游戏
大猫的无限游戏
Apple Machine Learning Research
Apple Machine Learning Research
V
V2EX
D
Docker
MongoDB | Blog
MongoDB | Blog
B
Blog RSS Feed
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
宝玉的分享
宝玉的分享
Engineering at Meta
Engineering at Meta
The Cloudflare Blog
博客园 - 三生石上(FineUI控件)
有赞技术团队
有赞技术团队
人人都是产品经理
人人都是产品经理
H
Help Net Security
T
The Blog of Author Tim Ferriss

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
Is Parallel Programming Hard, And, If So, What Can You Do...
Paul E. McKenney · 2017-01-04 · via cs.DC updates on arXiv.org

The purpose of this book is to help you program shared-memory parallel systems without risking your sanity. Nevertheless, you should think of the information in this book as a foundation on which to build, rather than as a completed cathedral. Your mission, if you choose to accept, is to help make further progress in the exciting field of parallel programming-progress that will in time render this book obsolete. Parallel programming in the 21st century is no longer focused solely on science, research, and grand-challenge projects. And this is all to the good, because it means that parallel programming is becoming an engineering discipline. Therefore, as befits an engineering discipline, this book examines specific parallel-programming tasks and describes how to approach them. In some surprisingly common cases, these tasks can be automated. This book is written in the hope that presenting the engineering discipline underlying successful parallel-programming projects will free a new generation of parallel hackers from the need to slowly and painstakingly reinvent old wheels, enabling them to instead focus their energy and creativity on new frontiers. However, what you get from this book will be determined by what you put into it. It is hoped that simply reading this book will be helpful, and that working the Quick Quizzes will be even more helpful. However, the best results come from applying the techniques taught in this book to real-life problems. As always, practice makes perfect. But no matter how you approach it, we sincerely hope that parallel programming brings you at least as much fun, excitement, and challenge that it has brought to us!