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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
Non-Interference and Local Correctness in Transactional M...
Petr Kuznetsov, Sathya Peri · 2012-11-27 · via cs.DS updates on arXiv.org

Transactional memory promises to make concurrent programming tractable and efficient by allowing the user to assemble sequences of actions in atomic transactions with all-or-nothing semantics. It is believed that, by its very virtue, transactional memory must ensure that all committed transactions constitute a serial execution respecting the real-time order. In contrast, aborted or incomplete transactions should not "take effect." But what does "not taking effect" mean exactly? It seems natural to expect that aborted or incomplete transactions do not appear in the global serial execution, and, thus, no committed transaction can be affected by them. We investigate another, less obvious, feature of "not taking effect" called non-interference: aborted or incomplete transactions should not force any other transaction to abort. In the strongest form of non-interference that we explore in this paper, by removing a subset of aborted or incomplete transactions from the history, we should not be able to turn an aborted transaction into a committed one without violating the correctness criterion. We show that non-interference is, in a strict sense, not implementable with respect to the popular criterion of opacity that requires all transactions (be they committed, aborted or incomplete) to witness the same global serial execution. In contrast, when we only require local correctness, non-interference is implementable. Informally, a correctness criterion is local if it only requires that every transaction can be serialized along with (a subset of) the transactions committed before its last event (aborted or incomplete transactions ignored). We give a few examples of local correctness properties, including the recently proposed criterion of virtual world consistency, and present a simple though efficient implementation that satisfies non-interference and local opacity.