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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
Moderately Complex Paxos Made Simple: High-Level Executab...
Yanhong A. Liu, Saksham Chand, Scott D. Stoller · 2017-04-01 · via cs.DC updates on arXiv.org

This paper describes the application of a high-level language and method in developing simpler specifications of more complex variants of the Paxos algorithm for distributed consensus. The specifications are for Multi-Paxos with preemption, replicated state machine, and reconfiguration and optimized with state reduction and failure detection. The language is DistAlgo. The key is to express complex control flows and synchronization conditions precisely at a high level, using nondeterministic waits and message-history queries. We obtain complete executable specifications that are almost completely declarative---updating only a number for the protocol round besides the sets of messages sent and received. We show the following results: 1.English and pseudocode descriptions of distributed algorithms can be captured completely and precisely at a high level, without adding, removing, or reformulating algorithm details to fit lower-level, more abstract, or less direct languages. 2.We created higher-level control flows and synchronization conditions than all previous specifications, and obtained specifications that are much simpler and smaller, even matching or smaller than abstract specifications that omit many algorithm details. 3.The simpler specifications led us to easily discover useless replies, unnecessary delays, and liveness violations (if messages can be lost) in previous published specifications, by just following the simplified algorithm flows. 4.The resulting specifications can be executed directly, and we can express optimizations cleanly, yielding drastic performance improvement over naive execution and facilitating a general method for merging processes. 5.We systematically translated the resulting specifications into TLA+ and developed machine-checked safety proofs, which also allowed us to detect and fix a subtle safety violation in an earlier unpublished specification.