Sequential and Shared-Memory Parallel Algorithms for Partitioned Local Depths

TL;DR

This paper introduces new sequential and shared-memory parallel algorithms for partitioned local depths (PaLD), a method for analyzing community structures based on pairwise distances, with proven optimality and significant speedups.

Contribution

It presents novel algorithmic variants for PaLD, provides theoretical analysis of their costs, and demonstrates substantial performance improvements on multicore CPUs.

Findings

Sequential algorithms are communication optimal up to constant factors.

Sequential speedups of up to 29x over baseline implementations.

Parallel speedups of up to 19.4x using 32 threads.

Abstract

In this work, we design, analyze, and optimize sequential and shared-memory parallel algorithms for partitioned local depths (PaLD). Given a set of data points and pairwise distances, PaLD is a method for identifying strength of pairwise relationships based on relative distances, enabling the identification of strong ties within dense and sparse communities even if their sizes and within-community absolute distances vary greatly. We design two algorithmic variants that perform community structure analysis through triplet comparisons of pairwise distances. We present theoretical analyses of computation and communication costs and prove that the sequential algorithms are communication optimal, up to constant factors. We introduce performance optimization strategies that yield sequential speedups of up to $29\times$ over a baseline sequential implementation and parallel speedups of up to $19.4\times$ over optimized sequential implementations using up to $32$ threads on an Intel multicore CPU.