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