A Parallel Min-Cut Algorithm using Iteratively Reweighted Least Squares

TL;DR

This paper introduces a parallel algorithm for the undirected s,t-mincut problem that leverages iteratively reweighted least squares and parallel Laplacian solvers, achieving significant speedups over serial methods.

Contribution

It presents a novel parallel min-cut algorithm based on iteratively reweighted least squares and parallel Laplacian solvers, improving scalability and efficiency.

Findings

Up to 30x faster than serial solvers with 128 cores

Uses iteratively reweighted least squares framework

Employs parallel matrix algorithms for Laplacian systems

Abstract

We present a parallel algorithm for the undirected $s,t$-mincut problem with floating-point valued weights. Our overarching algorithm uses an iteratively reweighted least squares framework. This generates a sequence of Laplacian linear systems, which we solve using parallel matrix algorithms. Our overall implementation is up to 30-times faster than a serial solver when using 128 cores.