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

博客园 - Franky
雷峰网
雷峰网
The Cloudflare Blog
WordPress大学
WordPress大学
博客园 - 聂微东
人人都是产品经理
人人都是产品经理
IT之家
IT之家
V
V2EX
博客园 - 司徒正美
小众软件
小众软件
博客园_首页
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
酷 壳 – CoolShell
酷 壳 – CoolShell
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Hugging Face - Blog
Hugging Face - Blog
T
Tailwind CSS Blog
Last Week in AI
Last Week in AI
Jina AI
Jina AI
博客园 - 叶小钗
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
阮一峰的网络日志
阮一峰的网络日志
爱范儿
爱范儿

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
Workload-Aware Autotuning of Block Size in Square-Root De...
[Submitted on 4 Jun 2026 (v1), last revised 28 Jul 2026 (this ve · 2026-06-04 · via cs.DS updates on arXiv.org

View PDF HTML (experimental)

Abstract:The textbook choice B=sqrt(n) for square-root decomposition is asymptotically natural, but it is not always the fastest implementation choice. We study block-size autotuning as a reproducible algorithm-engineering problem. On 3,840 generated workloads with repeated grouped cross-validation, the selected KNN-9 policy reduces mean regret from 1.2769 to 1.0478 and yields a paired geometric-mean speedup of 1.158x over fixed sqrt(n) blocking. A separate Docker/Linux confirmation over 960 workloads gives the same direction of effect (1.112x). We retain two trace-derived checks: the six-window Baleen study improves by 1.104x, whereas the three-workload Wikidata smoke study is slower than fixed blocking. These contrasting results delimit the contribution: workload-aware selection can improve an already chosen block-decomposition implementation, but it is neither a universal replacement for fixed blocking nor a replacement for BIT.

Submission history

From: Ruize Zhao [view email]
[v1] Thu, 4 Jun 2026 13:22:11 UTC (213 KB)
[v2] Tue, 28 Jul 2026 03:38:33 UTC (191 KB)