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

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 GitHub Blog
The GitHub Blog
J
Java Code Geeks
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
A
About on SuperTechFans
博客园 - 三生石上(FineUI控件)
罗磊的独立博客
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
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
有赞技术团队
有赞技术团队
T
Tailwind CSS Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
腾讯CDC
博客园 - 聂微东
The Cloudflare Blog
F
Fortinet All Blogs
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
Last Week in AI
Last Week in AI
B
Blog

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
Achieving Starvation-Freedom in Multi-Version Transaction...
Ved Prakash Chaudhary, Chirag Juyal, Sandeep Kulkarni, Sweta Kum · 2017-09-05 · via cs.DC updates on arXiv.org

Software Transactional Memory systems (STMs) have garnered significant interest as an elegant alternative for addressing synchronization and concurrency issues with multi-threaded programming in multi-core systems. Client programs use STMs by issuing transactions. STM ensures that transaction either commits or aborts. A transaction aborted due to conflicts is typically re-issued with the expectation that it will complete successfully in a subsequent incarnation. However, many existing STMs fail to provide starvation freedom, i.e., in these systems, it is possible that concurrency conflicts may prevent an incarnated transaction from committing. To overcome this limitation, we systematically derive a novel starvation free algorithm for multi-version STM. Our algorithm can be used either with the case where the number of versions is unbounded and garbage collection is used or where only the latest K versions are maintained, KSFTM. We have demonstrated that our proposed algorithm performs better than existing state-of-the-art STMs.