Searching for geometric objects that are close in space is a fundamental component of many applications. The performance of search algorithms comes to the forefront as the size of a problem increases both in terms of total object count as well as in the total number of search queries performed. Scientific applications requiring modern leadership-class supercomputers also pose an additional requirement of performance portability, i.e. being able to efficiently utilize a variety of hardware architectures. In this paper, we introduce a new open-source C++ search library, ArborX, which we have designed for modern supercomputing architectures. We examine scalable search algorithms with a focus on performance, including a highly efficient parallel bounding volume hierarchy implementation, and propose a flexible interface making it easy to integrate with existing applications. We demonstrate the performance portability of ArborX on multi-core CPUs and GPUs, and compare it to the state-of-the-art libraries such as Boost.Geometry.Index and nanoflann.
@article{arxiv.1908.11807,
title = {ArborX: A Performance Portable Geometric Search Library},
author = {D. Lebrun-Grandié and A. Prokopenko and B. Turcksin and S. R. Slattery},
journal= {arXiv preprint arXiv:1908.11807},
year = {2022}
}