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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
Strong Linearizability without Compare&Swap: The Case...
Faith Ellen, Gal Sela · 2024-11-29 · via cs.DS updates on arXiv.org

Because strongly-linearizable objects provide stronger guarantees than linearizability, they serve as valuable building blocks for the design of concurrent data structures. Yet, many objects that have linearizable implementations from base objects weaker than compare&swap objects do not have strongly-linearizable implementations from the same base objects. We focus on one such object: the bag, a multiset from which processes can take unspecified elements. We present the first lock-free, strongly-linearizable implementation of a bag from interfering objects (specifically, registers, and test&set objects). This may be surprising, since there are provably no such implementations of stacks or queues. Since a bag can contain arbitrarily many elements, an unbounded amount of space must be used to implement it. Hence, it makes sense to also consider a bag with a bound on its capacity. However, like stacks and queues, a bag with capacity $b$ shared by more than $2b$ processes has no lock-free, strongly-linearizable implementation from interfering objects. If we further restrict a bounded bag so that only one process can insert into it, we are able to obtain a lock-free, strongly-linearizable implementation from $O(b + n)$ interfering objects, where $n$ is the number of processes. Our goal is to understand the circumstances under which strongly-linearizable implementations of bags exist and, more generally, to understand the power of interfering objects.