ArborX: A Performance Portable Geometric Search Library

Abstract

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.

References in corpus (2)

• PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures
• Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units

Cited by in corpus (7)

• Enabling particle applications for exascale computing platforms
• Simulating Hydrodynamics in Cosmology with CRK-HACC
• Fast tree-based algorithms for DBSCAN for low-dimensional data on GPUs
• A single-tree algorithm to compute the Euclidean minimum spanning tree on GPUs
• PANDORA: A Parallel Dendrogram Construction Algorithm for Single Linkage Clustering on GPU
• Advances in ArborX to support exascale applications
• The ArborX library: version 2.0