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

GbyAI
GbyAI
博
博客园 - 三生石上(FineUI控件)
罗
罗磊的独立博客
P
Proofpoint News Feed
博
博客园_首页
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
D
Darknet – Hacking Tools, Hacker News & Cyber Security
月光博客
月光博客
L
LINUX DO - 热门话题
C
CERT Recently Published Vulnerability Notes
H
Hacker News: Front Page
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
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
G
Google Developers Blog
博
博客园 - 【当耐特】
C
Cisco Blogs
博
博客园 - 叶小钗
Apple Machine Learning Research
Apple Machine Learning Research
T
Tor Project blog
C
CXSECURITY Database RSS Feed - CXSecurity.com
The Last Watchdog
The Last Watchdog
腾
腾讯CDC
P
Palo Alto Networks Blog
F
Full Disclosure
S
SegmentFault 最新的问题
Simon Willison's Weblog
Simon Willison's Weblog
C
Cyber Attacks, Cyber Crime and Cyber Security
Recent Announcements
Recent Announcements
P
Privacy & Cybersecurity Law Blog
Security Archives - TechRepublic
Security Archives - TechRepublic
PCI Perspectives
PCI Perspectives
A
About on SuperTechFans
www.infosecurity-magazine.com
www.infosecurity-magazine.com
The Register - Security
The Register - Security
IT之家
IT之家
人人都是产品经理
人人都是产品经理
博
博客园 - 司徒正美
T
Tailwind CSS Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
I
InfoQ
Webroot Blog
Webroot Blog
S
Security Affairs
T
The Blog of Author Tim Ferriss
T
Threat Research - Cisco Blogs
量
量子位
Hugging Face - Blog
Hugging Face - Blog
F
Fortinet All Blogs
美
美团技术团队
Cyberwarzone
Cyberwarzone
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events

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 Preserving Clusters in Error-Bounded Lossy Compression of Particle Data Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM UCCL-Zip: Lossless Compression Supercharged GPU Communication Training Time Prediction for Mixed Precision-based Distributed Training Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling Optimizing Stochastic Gradient Push under Broadcast Communications Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines ARGUS: Agentic GPU Optimization Guided by Data-Flow Invariants Prefill-as-a-Service: KVCache of Next-Generation Models Could Go Cross-Datacenter Cooperate to Compete: Strategic Data Generation and Incentivization Framework for Coopetitive Cross-Silo Federated Learning Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving AgileLog: A Forkable Shared Log for Agents on Data Streams Secure and Privacy-Preserving Vertical Federated Learning Event Tensor: A Unified Abstraction for Compiling Dynamic Megakernel CUTEv2: Unified and Configurable Matrix Extension for Diverse CPU Architectures with Minimal Design Overhead Record-Remix-Replay: Hierarchical GPU Kernel Optimization using Evolutionary Search NimbusGuard: A Novel Framework for Proactive Kubernetes Autoscaling Using Deep Q-Networks Taming Asynchronous CPU-GPU Coupling for Frequency-aware Latency Estimation on Mobile Edge Rebooting Microreboot: Architectural Support for Safe, Parallel Recovery in Microservice Systems A-IO: Adaptive Inference Orchestration for Memory-Bound NPUs SMART: When is it Actually Worth Expanding a Speculative Tree? ConfigSpec: Profiling-Based Configuration Selection for Distributed Edge--Cloud Speculative LLM Serving OpenCLAW-P2P v7.0-P2PCLAW: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review v7.0 -- Mathematical Corrections & Ecosystem Developments Edition DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection HearthNet: Edge Multi-Agent Orchestration for Smart Homes Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios Duration-Informed Workload Scheduler Domain-Adaptive Model Merging Across Disconnected Modes Why Smaller Is Slower? Dimensional Misalignment in Compressed LLMs veScale-FSDP: Flexible and High-Performance FSDP at Scale AEG: A Baremetal Framework for AI Acceleration via Direct Hardware Access in Heterogeneous Accelerators ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness StreamServe: Adaptive Speculative Flows for Low-Latency Disaggregated LLM Serving Emergent Social Structures in Autonomous AI Agent Networks: A Metadata Analysis of 626 Agents on the Pilot Protocol SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding Para-B&B: Load-Balanced Deterministic Parallelization of Solving MIP Rashomon Sets and Model Multiplicity in Federated Learning Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers Scalable Explainability-as-a-Service (XaaS) for Edge AI Systems NPU Design for Diffusion Language Model Inference PRAXIS: Integrating Program Analysis with Observability for Root-Cause Analysis BitFlipScope: Scalable Fault Localization and Recovery for Bit-Flip Corruptions in LLMs Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving SHARe-KAN: Post-Training Vector Quantization for Cache-Resident KAN Inference Spira: Exploiting Voxel Data Structural Properties for Efficient Sparse Convolution in Point Cloud Networks Power to the Clients: Federated Learning in a Dictatorship Setting From Tokens to Layers: Redefining Stall-Free Scheduling for MoE Serving with Layered Prefill Speculative Actions: A Lossless Framework for Faster Agentic Systems InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling HFX: Joint Design of Algorithms and Systems for Multi-SLO Serving and Fast Scaling Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batching Deep Optimizer States: Towards Scalable Training of Transformer Models Using Interleaved Offloading CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Execution of Compound Multi-Kernel OpenCL Computations in Multi-CPU/Multi-GPU Environments
Fábio Soldado, Fernando Alexandre, Hervé Paulino · 2015-10-22 · via cs.DC updates on arXiv.org

Current computational systems are heterogeneous by nature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming of these hybrid systems as a whole. The distinct nature of the architectural and execution models in place raises several challenges, as the best hardware configuration is behaviour and workload dependent. In this paper, we address the execution of compound, multi-kernel, OpenCL computations in multi-CPU/multi-GPU environments. We address how these computations may be efficiently scheduled onto the target hardware, and how the system may adapt itself to changes in the workload to process and to fluctuations in the CPU's load. An experimental evaluation attests the performance gains obtained by the conjoined use of the CPU and GPU devices, when compared to GPU-only executions, and also by the use of data-locality optimizations in CPU environments.

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。