The Algorithm of Pipelined Gossiping

TL;DR

This paper introduces a family of gossiping algorithms with a focus on pipelined performance, analyzing their asymptotic behavior and proposing an optimization strategy to enhance efficiency.

Contribution

It formalizes a new family of gossiping algorithms based on parameter permutations and analyzes their asymptotic performance, including a pipelined processor-like model.

Findings

Several algorithms analyzed for asymptotic behavior

Identification of a member resembling hardware pipelined processors

Proposed optimization algorithm improves performance

Abstract

A family of gossiping algorithms depending on a parameter permutation is introduced, formalized, and discussed. Several of its members are analyzed and their asymptotic behaviour is revealed, including a member whose model and performance closely follows the one of hardware pipelined processors. This similarity is exposed. An optimizing algorithm is finally proposed and discussed as a general strategy to increase the performance of the base algorithms.