Parallel Approximation Algorithms for Facility-Location Problems

TL;DR

This paper develops parallel approximation algorithms for facility-location problems, achieving low-depth, near work efficiency, and cache efficiency, thus enabling faster solutions while maintaining quality guarantees.

Contribution

It introduces parallel algorithms for facility-location problems that are efficient in depth, work, and cache complexity, extending sequential approximation results to parallel settings.

Findings

Algorithms have low depth and near work efficiency.

Cache complexity is bounded by O(w/B).

Parallel algorithms maintain approximation guarantees.

Abstract

This paper presents the design and analysis of parallel approximation algorithms for facility-location problems, including $\NC$ and $\RNC$ algorithms for (metric) facility location, $k$-center, $k$-median, and $k$-means. These problems have received considerable attention during the past decades from the approximation algorithms community, concentrating primarily on improving the approximation guarantees. In this paper, we ask, is it possible to parallelize some of the beautiful results from the sequential setting? Our starting point is a small, but diverse, subset of results in approximation algorithms for facility-location problems, with a primary goal of developing techniques for devising their efficient parallel counterparts. We focus on giving algorithms with low depth, near work efficiency (compared to the sequential versions), and low cache complexity. Common in algorithms we present is the idea that instead of picking only the most cost-effective element, we make room for parallelism by allowing a small slack (e.g., a $(1+\vareps)$ factor) in what can be selected---then, we use a clean-up step to ensure that the behavior does not deviate too much from the sequential steps. All the algorithms we developed are ``cache efficient'' in that the cache complexity is bounded by $O(w/B)$, where $w$ is the work in the EREW model and $B$ is the block size.