Coarse Grained Parallel Selection

TL;DR

This paper evaluates the Saukas-Song selection algorithm's efficiency on coarse grained multicomputers, revealing its optimality only in best cases, and introduces new algorithms with guaranteed optimal expected running time.

Contribution

The paper analyzes the Saukas-Song algorithm's performance and proposes new algorithms with proven optimal expected running time for coarse grained selection.

Findings

Saukas-Song algorithm is asymptotically optimal in best case scenarios.

In general, Saukas-Song does not have optimal running time.

New algorithms are introduced with guaranteed optimal expected running time.

Abstract

We analyze the running time of the Saukas-Song algorithm for selection on a coarse grained multicomputer without expressing the running time in terms of communication rounds. This shows that while in the best case the Saukas-Song algorithm runs in asymptotically optimal time, in general it does not. We propose other algorithms for coarse grained selection that have optimal expected running time.