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 HPC. In this 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 HPC, 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. While we find that there is no single best solution, three libraries, Boost, CGAL and R-tree, offer some of the best performance, scalability, memory overheads, and support for different mesh types. We find several areas where the spatial indices could be substantially improved: better performance when there are a large number of query matches, reduced memory overheads, and better support for GPUs or other accelerators.

High-performance computing scientists are producing unprecedented volumes of data that take a long time to load for analysis. However, many analyses only require loading in the data containing particular features of interest and scientists have many approaches for identifying these features. Therefore, if scientists store information (descriptive metadata) about these identified features, then for subsequent analyses they can use this information to only read in the data containing these features. This can greatly reduce the amount of data that scientists have to read in, thereby accelerating analysis. Despite the potential benefits of descriptive metadata management, no prior work has created a descriptive metadata system that can help scientists working with a wide range of applications and analyses to restrict their reads to data containing features of interest. In this article, we present EMPRESS, the first such solution. EMPRESS offers all of the features needed to help accelerate discovery: It can accelerate analysis by up to 300 ×, supports a wide range of applications and analyses, is high-performing, is highly scalable, and requires minimal storage space. In addition, EMPRESS offers features required for a production-oriented system: scalable metadata consistency techniques, flexible system configurations, fault tolerance as a service, and portability.