Exploring Spatial Indexing for Accelerated Feature Retrieval in HPC

TL;DR

This paper evaluates 20 open-source spatial indexing libraries to determine their suitability for HPC range queries, comparing performance, scalability, and trade-offs to guide optimal selection.

Contribution

It provides the first comprehensive analysis of spatial index libraries for HPC, assessing their viability, performance metrics, and trade-offs in an HPC context.

Findings

Some libraries are viable in HPC environments.

Build time and query performance vary significantly.

Brute force can outperform indices in certain scenarios.

Abstract

Despite the critical role that range queries play in analysis and visualization for HPC applications, there has been no comprehensive analysis of indices that are designed to accelerate range queries and the extent to which they are viable in an HPC setting. In this state of the practice paper we present the first such evaluation, examining 20 open-source C and C++ libraries that support range queries. Contributions of this paper include answering the following questions: which of the implementations are viable in an HPC setting, how do these libraries compare in terms of build time, query time, memory usage, and scalability, what are other trade-offs between these implementations, is there a single overall best solution, and when does a brute force solution offer the best performance? We also share key insights learned during this process that can assist both HPC application scientists and spatial index developers.