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

Hugging Face - Blog
Hugging Face - Blog
Vercel News
Vercel News
C
Check Point Blog
G
Google Developers Blog
博客园 - 司徒正美
量子位
Engineering at Meta
Engineering at Meta
S
SegmentFault 最新的问题
Google DeepMind News
Google DeepMind News
F
Fortinet All Blogs
A
About on SuperTechFans
美团技术团队
D
DataBreaches.Net
Stack Overflow Blog
Stack Overflow Blog
Jina AI
Jina AI
Y
Y Combinator Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
J
Java Code Geeks
MongoDB | Blog
MongoDB | Blog
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The Cloudflare Blog
U
Unit 42

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
Two Efficient Message-passing Exclusive Scan Algorithms
Jesper Larsson Träff · 2026-04-28 · via cs.DS updates on arXiv.org

Parallel scan primitives compute element-wise inclusive or exclusive prefix sums of input vectors contributed by $p$ consecutively ranked processors under an associative, possibly expensive, binary operator $\oplus$. In message-passing systems with bounded, one-ported communication capabilities, at least $\lceil\log_2 p\rceil$ or $\lceil\log_2 (p-1)\rceil$ send-receive communication rounds are required to perform the scans. While there are well-known, simple algorithms for the inclusive scan that solve the problem in $\lceil\log_2 p\rceil$ send-receive communication rounds with $\lceil\log_2 p\rceil$ applications of the $\oplus$ operator, the exclusive scan is different and has been much less addressed. By considering natural invariants for the exclusive prefix sums problem, we present two different algorithms that are efficient in the number of communication rounds and in the number of applications of the $\oplus$ operator. The first algorithm consists of an inclusive scan phase and an exclusive scan phase and trades the number of communication rounds against the number of applications of the $\oplus$ operator. The smallest number of inclusive scan rounds with $q=\lceil\log_2 p\rceil$ rounds in total is $q'\geq q-\log_2(2^q-p+1)$. The other algorithm is a modification of a round-optimal all-reduce algorithm, and the number of additional applications of the $\oplus$ operator is dependent on the number of bits set (popcount of) in $p-1$. Both algorithms are relevant for small(er) input vectors where performance is dominated by the number of communication rounds. For large input vectors, other (pipelined, fixed-degree tree) algorithms must be used.