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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
AutoSynch: An Automatic-Signal Monitor Based on Predicate...
Wei-Lun Hung, Vijay K. Garg · 2013-03-02 · via cs.DC updates on arXiv.org

Most programming languages use monitors with explicit signals for synchronization in shared-memory programs. Requiring program- mers to signal threads explicitly results in many concurrency bugs due to missed notifications, or notifications on wrong condition variables. In this paper, we describe an implementation of an au- tomatic signaling monitor in Java called AutoSynch that eliminates such concurrency bugs by removing the burden of signaling from the programmer. We show that the belief that automatic signaling monitors are prohibitively expensive is wrong. For most problems, programs based on AutoSynch are almost as fast as those based on explicit signaling. For some, AutoSynch is even faster than explicit signaling because it never uses signalAll, whereas the programmers end up using signalAll with the explicit signal mechanism. AutoSynch achieves efficiency in synchronization based on three novel ideas. We introduce an operation called globalization that enables the predicate evaluation in every thread, thereby reducing context switches during the execution of the program. Secondly, AutoSynch avoids signalAll by using a property called relay invari- ance that guarantees that whenever possible there is always at least one thread whose condition is true which has been signaled. Finally, AutoSynch uses a technique called predicate tagging to efficiently determine a thread that should be signaled. To evaluate the effi- ciency of AutoSynch, we have implemented many different well- known synchronization problems such as the producers/consumers problem, the readers/writers problems, and the dining philosophers problem. The results show that AutoSynch is almost as efficient as the explicit-signal monitor and even more efficient for some cases.