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

博客园 - Franky
云风的 BLOG
云风的 BLOG
人人都是产品经理
人人都是产品经理
博客园 - 叶小钗
Engineering at Meta
Engineering at Meta
Vercel News
Vercel News
Y
Y Combinator Blog
B
Blog
Microsoft Azure Blog
Microsoft Azure Blog
C
Check Point Blog
M
MIT News - Artificial intelligence
Jina AI
Jina AI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Apple Machine Learning Research
Apple Machine Learning Research
Hugging Face - Blog
Hugging Face - Blog
阮一峰的网络日志
阮一峰的网络日志
罗磊的独立博客
Stack Overflow Blog
Stack Overflow Blog
F
Fortinet All Blogs
博客园 - 司徒正美
I
InfoQ
Google DeepMind News
Google DeepMind News
GbyAI
GbyAI
U
Unit 42

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
A high-level C++ approach to manage local errors, asynchr...
Christian Engwer, Mirco Altenbernd, Nils-Arne Dreier, Dominik Gö · 2018-04-12 · via cs.DC updates on arXiv.org

C++ advocates exceptions as the preferred way to handle unexpected behaviour of an implementation in the code. This does not integrate well with the error handling of MPI, which more or less always results in program termination in case of MPI failures. In particular, a local C++ exception can currently lead to a deadlock due to unfinished communication requests on remote hosts. At the same time, future MPI implementations are expected to include an API to continue computations even after a hard fault (node loss), i.e. the worst possible unexpected behaviour. In this paper we present an approach that adds extended exception propagation support to C++ MPI programs. Our technique allows to propagate local exceptions to remote hosts to avoid deadlocks, and to map MPI failures on remote hosts to local exceptions. A use case of particular interest are asynchronous 'local failure local recovery' resilience approaches. Our prototype implementation uses MPI-3.0 features only. In addition we present a dedicated implementation, which integrates seamlessly with MPI-ULFM, i.e. the most prominent proposal for extending MPI towards fault tolerance. Our implementation is available at https://gitlab.dune-project.org/christi/test-mpi-exceptions .