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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? 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Causing Communication Closure: Safe Program Composition with Reliable Non-FIFO Channels
Kai Engelhardt, Yoram Moses · 2007-01-09 · via cs.DC updates on arXiv.org

A semantic framework for analyzing safe composition of distributed programs is presented. Its applicability is illustrated by a study of program composition when communication is reliable but not necessarily FIFO\@. In this model, special care must be taken to ensure that messages do not accidentally overtake one another in the composed program. We show that barriers do not exist in this model. Indeed, no program that sends or receives messages can automatically be composed with arbitrary programs without jeopardizing their intended behavior. Safety of composition becomes context-sensitive and new tools are needed for ensuring it. A notion of \emph{sealing} is defined, where if a program $P$ is immediately followed by a program $Q$ that seals $P$ then $P$ will be communication-closed--it will execute as if it runs in isolation. The investigation of sealing in this model reveals a novel connection between Lamport causality and safe composition. A characterization of sealable programs is given, as well as efficient algorithms for testing if $Q$ seals $P$ and for constructing a seal for a significant class of programs. It is shown that every sealable program that is open to interference on $O(n^2)$ channels can be sealed using O(n) messages.

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