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

C
Check Point Blog
GbyAI
GbyAI
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 叶小钗
U
Unit 42
Engineering at Meta
Engineering at Meta
aimingoo的专栏
aimingoo的专栏
Y
Y Combinator Blog
Google DeepMind News
Google DeepMind News
Vercel News
Vercel News
美团技术团队
雷峰网
雷峰网
Recent Announcements
Recent Announcements
有赞技术团队
有赞技术团队
D
DataBreaches.Net
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Apple Machine Learning Research
Apple Machine Learning Research
J
Java Code Geeks
罗磊的独立博客
MyScale Blog
MyScale Blog
博客园_首页
IT之家
IT之家
F
Fortinet All Blogs
博客园 - Franky

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
Boosting the Search Performance of B+-tree for Non-volati...
Chongnan Ye, Chundong Wang · 2021-06-01 · via cs.DS updates on arXiv.org

The next-generation non-volatile memory (NVM) is striding into computer systems as a new tier as it incorporates both DRAM's byte-addressability and disk's persistency. Researchers and practitioners have considered building persistent memory by placing NVM on the memory bus for CPU to directly load and store data. As a result, cache-friendly data structures have been developed for NVM. One of them is the prevalent B+-tree. State-of-the-art in-NVM B+-trees mainly focus on the optimization of write operations (insertion and deletion). However, search is of vital importance for B+-tree. Not only search-intensive workloads benefit from an optimized search, but insertion and deletion also rely on a preceding search operation to proceed. In this paper, we attentively study a sorted B+-tree node that spans over contiguous cache lines. Such cache lines exhibit a monotonically increasing trend and searching a target key across them can be accelerated by estimating a range the key falls into. To do so, we construct a probing Sentinel Array in which a sentinel stands for each cache line of B+-tree node. Checking the Sentinel Array avoids scanning unnecessary cache lines and hence significantly reduces cache misses for a search. A quantitative evaluation shows that using Sentinel Arrays boosts the search performance of state-of-the-art in-NVM B+-trees by up to 48.4% while the cost of maintaining of Sentinel Array is low.