Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 83 tok/s
Gemini 2.5 Pro 34 tok/s Pro
GPT-5 Medium 24 tok/s Pro
GPT-5 High 21 tok/s Pro
GPT-4o 130 tok/s Pro
Kimi K2 207 tok/s Pro
GPT OSS 120B 460 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

Parallel multiple selection by regular sampling (1611.05549v2)

Published 17 Nov 2016 in cs.DC

Abstract: In this paper we present a deterministic parallel algorithm solving the multiple selection problem in congested clique model. In this problem for given set of elements S and a set of ranks $K = {k_1 , k_2 , ..., k_r }$ we are asking for the $k_i$-th smallest element of $S$ for $1 \leq i \leq r$. The presented algorithm is deterministic, time optimal , and needs $O(\log*_{r+1} (n))$ communication rounds, where $n$ is the size of the input set, and $r$ is the size of the rank set. This algorithm may be of theoretical interest, as for $r = 1$ (classic selection problem) it gives an improvement in the asymptotic synchronization cost over previous $O(\log \log p)$ communication rounds solution, where $p$ is size of clique.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.