A Parallel Batch-Dynamic Data Structure for the Closest Pair Problem

TL;DR

This paper introduces a parallel batch-dynamic data structure for the closest pair problem that is both theoretically efficient and practically fast, supporting large batches of updates with high parallel speedup.

Contribution

It presents the first practical parallel batch-dynamic data structure for the closest pair problem with proven work and depth bounds, and provides extensive experimental evaluation.

Findings

Achieves up to 38.57x speedup on 48 cores for dynamic updates.

Static algorithms reach up to 51.45x speedup, with Rabin's algorithm performing best.

Dynamic algorithm is more efficient for batch sizes up to 20% of data set.

Abstract

We propose a theoretically-efficient and practical parallel batch-dynamic data structure for the closest pair problem. Our solution is based on a serial dynamic closest pair data structure by Golin et al., and supports batches of insertions and deletions in parallel. For a data set of size $n$, our data structure supports a batch of insertions or deletions of size $m$ in $O(m(1+\log ((n+m)/m)))$ expected work and $O(\log (n+m)\log^*(n+m))$ depth with high probability, and takes linear space. The key techniques for achieving these bounds are a new work-efficient parallel batch-dynamic binary heap, and careful management of the computation across sets of points to minimize work and depth. We provide an optimized multicore implementation of our data structure using dynamic hash tables, parallel heaps, and dynamic $k$-d trees. Our experiments on a variety of synthetic and real-world data sets show that it achieves a parallel speedup of up to 38.57x (15.10x on average) on 48 cores with hyper-threading. In addition, we also implement and compare four parallel algorithms for static closest pair problem, for which we are not aware of any existing practical implementations. On 48 cores with hyper-threading, the static algorithms achieve up to 51.45x (29.42x on average) speedup, and Rabin's algorithm performs the best on average. Comparing our dynamic algorithm to the fastest static algorithm, we find that it is advantageous to use the dynamic algorithm for batch sizes of up to 20\% of the data set. As far as we know, our work is the first to experimentally evaluate parallel closest pair algorithms, in both the static and the dynamic settings.