D-iteration: Evaluation of a Dynamic Partition Strategy

TL;DR

This paper evaluates a dynamic partition strategy for the D-iteration method, demonstrating improved load balancing and efficiency in distributed linear equation solving without detailed matrix analysis.

Contribution

It introduces and assesses a simple dynamic partition strategy that enhances load balancing in distributed D-iteration computations.

Findings

Dynamic partitioning improves load balancing among virtual machines.

Memory usage per machine decreases linearly with the number of machines.

Computation speed increases nearly linearly with the number of machines.

Abstract

The aim of this paper is to present a first evaluation of a dynamic partition strategy associated to the recently proposed asynchronous distributed computation scheme based on the D-iteration approach. The D-iteration is a fluid diffusion point of view based iteration method to solve numerically linear equations. Using a simple static partition strategy, it has been shown that, when the computation is distributed over K virtual machines (PIDs), the memory size to be handled by each virtual machine decreases linearly with K and the computation speed increases almost linearly with K with a slope becoming closer to one when the number N of linear equations to be solved increases. Here, we want to evaluate how further those results can be improved when a simple dynamic partition strategy is deployed and to show that the dynamic partition strategy allows one to control and equalize the computation load between PIDs without any deep analysis of the matrix or of the underlying graph structure.