Almost Universally Optimal Distributed Laplacian Solvers via Low-Congestion Shortcuts

TL;DR

This paper advances distributed Laplacian solvers to be nearly universally optimal across various network topologies and models, leveraging novel congestion-aware algorithms for part-wise aggregation.

Contribution

It introduces a unifying framework for distributed Laplacian solving that achieves near-optimal rounds in multiple models, including known and unknown topologies, and develops new congestion-tolerant algorithms.

Findings

Achieves near-optimal round complexity for Laplacian solving in known-topology networks.

Extends results to unknown-topology networks in the CONGEST model.

Introduces a congestion-aware primitive for part-wise aggregation with broad applications.

Abstract

In this paper, we refine the (almost) \emph{existentially optimal} distributed Laplacian solver recently developed by Forster, Goranci, Liu, Peng, Sun, and Ye (FOCS `21) into an (almost) \emph{universally optimal} distributed Laplacian solver. Specifically, when the topology is known, we show that any Laplacian system on an $n$-node graph with \emph{shortcut quality} $\text{SQ}(G)$ can be solved within $n^{o(1)} \text{SQ}(G) \log(1/\varepsilon)$ rounds, where $\varepsilon$ is the required accuracy. This almost matches our lower bound which guarantees that any correct algorithm on $G$ requires $\widetilde{\Omega}(\text{SQ}(G))$ rounds, even for a crude solution with $\varepsilon \le 1/2$. Even in the unknown-topology case (i.e., standard CONGEST), the same bounds also hold in most networks of interest. Furthermore, conditional on conjectured improvements in state-of-the-art constructions of low-congestion shortcuts, the CONGEST results will match the known-topology ones. Moreover, following a recent line of work in distributed algorithms, we consider a hybrid communication model which enhances CONGEST with limited global power in the form of the node-capacitated clique (NCC) model. In this model, we show the existence of a Laplacian solver with round complexity $n^{o(1)} \log(1/\varepsilon)$. The unifying thread of these results, and our main technical contribution, is the study of novel \emph{congested} generalization of the standard \emph{part-wise aggregation} problem. We develop near-optimal algorithms for this primitive in the Supported-CONGEST model, almost-optimal algorithms in (standard) CONGEST, as well as a very simple algorithm for bounded-treewidth graphs with slightly worse bounds. This primitive can be readily used to accelerate the FOCS`21 Laplacian solver. We believe this primitive will find further independent applications.