惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

宝玉的分享
宝玉的分享
B
Blog RSS Feed
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
MyScale Blog
MyScale Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
S
SegmentFault 最新的问题
Y
Y Combinator Blog
月光博客
月光博客
IT之家
IT之家
T
Tailwind CSS Blog
Last Week in AI
Last Week in AI
L
LangChain Blog
博客园_首页
MongoDB | Blog
MongoDB | Blog
P
Proofpoint News Feed
博客园 - Franky
WordPress大学
WordPress大学
云风的 BLOG
云风的 BLOG
M
MIT News - Artificial intelligence
V
Visual Studio Blog
小众软件
小众软件
博客园 - 叶小钗
博客园 - 三生石上(FineUI控件)
N
Netflix TechBlog - Medium

cs.DS updates on arXiv.org

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
Efficient Partial Snapshot Implementations
Nikolaos D. Kallimanis, Eleni Kanellou, Charidimos Kiosterakis · 2020-06-11 · via cs.DS updates on arXiv.org

In this work, we propose the $λ$-scanner snapshot, a variation of the snapshot object, which supports any fixed amount of $0 < λ\leq n$ different $SCAN$ operations being active at any given time. Whenever $λ$ is equal to the number of processes $n$ in the system, the $λ$-scanner object implements a multi-scanner object, while in case that $λ$ is equal to $1$, the $λ$-scanner object implements a single-scanner object. We present the $λ-Snap$ snapshot object, a wait-free $λ$-scanner snapshot implementation that has a step complexity of $O(λ)$ for $UPDATE$ operations and $O(λm)$ for $SCAN$ operations. The space complexity of $λ-Snap$ is $O(λm)$. $λ-Snap$ provides a trade-off between the step/space complexity and the maximum number of $SCAN$ operations that the system can afford to be active on any given point in time. The low space complexity that our implementations provide makes them more appealing in real system applications. Moreover, we provide a slightly modified version of the $λ-Snap$ implementation, which is called partial $λ-Snap$, that is able to support dynamic partial scan operations. In such an object, processes can execute modified $SCAN$ operations called $PARTIAL\_SCAN$ that could obtain a part of the snapshot object avoiding to read the whole set of components. In this work, we first provide a simple single-scanner version of $λ-Snap$, which is called $1-Snap$. We provide $1-Snap$ just for presentation purposes, since it is simpler than $λ-Snap$. The $UPDATE$ in $1-Snap$ has a step complexity of $O(1)$, while the $SCAN$ has a step complexity of $O(m)$. This implementation uses $O(m)$ $CAS$ registers.