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

J
Java Code Geeks
月光博客
月光博客
aimingoo的专栏
aimingoo的专栏
Google DeepMind News
Google DeepMind News
Recent Announcements
Recent Announcements
MyScale Blog
MyScale Blog
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
S
SegmentFault 最新的问题
Hugging Face - Blog
Hugging Face - Blog
Martin Fowler
Martin Fowler
WordPress大学
WordPress大学
F
Fortinet All Blogs
小众软件
小众软件
D
Docker
U
Unit 42
博客园 - 聂微东
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
云风的 BLOG
云风的 BLOG
博客园 - 司徒正美
有赞技术团队
有赞技术团队
腾讯CDC

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
BUNDLEP: Prioritizing Conflict Free Regions in Multi-Thre...
Corey Tessler, Nathan Fisher · 2018-05-30 · via cs.DC updates on arXiv.org

In BUNDLE: Real-Time Multi-Threaded Scheduling to Reduce Cache Contention, Tessler and Fisher propose a scheduling mechanism and combined worst-case execution time calculation method that treats the instruction cache as a beneficial resource shared between threads. Object analysis produces a worst-case execution time bound and separates code segments into regions. Threads are dynamically placed in bundles as- sociated with regions at run time by the BUNDLE scheduling algorithm where they benefit from shared cache values. In the evaluation of the previous work, tasks were created with a predetermined worst-case execution time path through the control flow graph. Apriori knowledge of the worst-case path is an impractical restriction on any analysis. At the time, the only other solution available was an all-paths search of the graph, which is an equally impractical approach due to its complexity. The primary focus of this work is to build upon BUNDLE, expanding its applicability beyond a proof of concept. We present a complete a worst-case execution time calculation method that includes thread level context switch costs, operating on real programs, with representative architecture parameters, and compare our results to those produced by Heptane's state of the art method. To these ends, we propose a modification to the BUNDLE scheduling algorithm called BUNDLEP. Bundles are assigned priorities that enforce an ordered flow of threads through the control flow graph -- avoiding the need for multiple all-paths searches through the graph. In many cases, our evaluation shows a run-time and analytical benefit for BUNLDEP compared to serialized thread execution and state of the art WCET analysis.