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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
NBR: Neutralization Based Reclamation
Ajay Singh, Trevor Brown, Ali Mashtizadeh · 2020-12-29 · via cs.DS updates on arXiv.org

Safe memory reclamation (SMR) algorithms suffer from a trade-off between bounding unreclaimed memory and the speed of reclamation. Hazard pointer (HP) based algorithms bound unreclaimed memory at all times, but tend to be slower than other approaches. Epoch based reclamation (EBR) algorithms are faster, but do not bound memory reclamation. Other algorithms follow hybrid approaches, requiring special compiler or hardware support, changes to record layouts, and/or extensive code changes. Not all SMR algorithms can be used to reclaim memory for all data structures. We propose a new neutralization based reclamation (NBR) algorithm that is faster than the best known EBR algorithms and achieves bounded unreclaimed memory. It is non-blocking when used with a non-blocking operating system (OS) kernel, and only requires atomic read, write and CAS. NBR is straightforward to use with many different data structures, and in most cases, require similar reasoning and programmer effort to two-phased locking. NBR is implemented using OS signals and a lightweight handshaking mechanism between participating threads to determine when it is safe to reclaim a record. Experiments on a lock-based binary search tree and a lazy linked list show that NBR significantly outperforms many state of the art reclamation algorithms. In the tree NBR is faster than next best algorithm, DEBRA by upto 38% and HP by upto 17%. And, in the list NBR is 15% and 243% faster than DEBRA and HP, respectively.