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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
No Cords Attached: Coordination-Free Concurrent Lock-Free...
Yusuf Motiwala · 2025-11-12 · via cs.DS updates on arXiv.org

The queue is conceptually one of the simplest data structures-a basic FIFO container. However, ensuring correctness in the presence of concurrency makes existing lock-free implementations significantly more complex than their original form. Coordination mechanisms introduced to prevent hazards such as ABA, use-after-free, and unsafe reclamation often dominate the design, overshadowing the queue itself. Many schemes compromise strict FIFO ordering, unbounded capacity, or lock-free progress to mask coordination overheads. Yet the true source of complexity lies in the pursuit of infinite protection against reclamation hazards--theoretically sound but impractical and costly. This pursuit not only drives unnecessary complexity but also creates a protection paradox where excessive protection reduces system resilience rather than improving it. While such costs may be tolerable in conventional workloads, the AI era has shifted the paradigm: training and inference pipelines involve hundreds to thousands of concurrent threads per node, and at this scale, protection and coordination overheads dominate, often far heavier than the basic queue operations themselves. This paper introduces Cyclic Memory Protection (CMP), a coordination-free queue that preserves strict FIFO semantics, unbounded capacity, and lock-free progress while restoring simplicity. CMP reclaims the strict FIFO that other approaches sacrificed through bounded protection windows that provide practical reclamation guarantees. We prove strict FIFO and safety via linearizability and bounded reclamation analysis, and show experimentally that CMP outperforms state-of-the-art lock-free queues by up to 1.72-4x under high contention while maintaining scalability to hundreds of threads. Our work demonstrates that highly concurrent queues can return to their fundamental simplicity without weakening queue semantics.