Advances in ArborX to support exascale applications

TL;DR

This paper discusses enhancements to ArborX, a geometric search library, enabling efficient, portable DBSCAN clustering for large-scale cosmological simulations on exascale supercomputers.

Contribution

The paper introduces new algorithmic improvements and implementations of DBSCAN in ArborX, tailored for exascale applications and performance portability across different supercomputing platforms.

Findings

Improved search index algorithms increase performance.

Enhanced ArborX supports large-scale, in-situ cosmological analysis.

Demonstrated real-world impact on production cosmology simulations.

Abstract

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations.