Search NASASearch

DOE OSTI · 3009445

The ArborX Library: Version 2.0

Abstract

This article provides an overview of the 2.0 release of the ArborX library, a performance portable geometric search library based on Kokkos. We describe the major changes in ArborX 2.0 including a new interface for the library to support a wider range of user problems, new search data structures (brute force and distributed), support for user functions to be executed on the results (callbacks), and an expanded set of the supported algorithms (ray tracing and clustering).

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Prokopenko, Andrey [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000336165504), Arndt, Daniel [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000187734901), Lebrun-Grandié, Damien [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000319527219), Turcksin, Bruno [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000159546313). 2025-12-12. The ArborX Library: Version 2.0. https://doi.org/10.1145/3772288

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NEML2: An efficient and modular multiphysics constitutive modeling library for hybrid computing environments

This paper presents NEML2, an open-source, high-performance library developed for constitutive material modeling, designed to support the flexible and modular development of models for complex material behavior. Building on the foundational structure of its predecessor, NEML, the NEML2 library introduces significant improvements, including enhanced vectorization, automatic differentiation, and seamless integration with PyTorch, facilitating the application of machine learning techniques in material simulations. NEML2 provides a C++ backend with Python bindings, enabling users to create custom material models that can be executed efficiently on both CPU and GPU platforms. The library also supports coupling with Multiphysics simulation frameworks like MOOSE, making it suitable for realistic simulations involving coupled physical processes. Rigorous quality assurance through unit and regression testing ensures the reliability of results, while the extensible, user-friendly design encourages collaboration and reproducibility across the scientific community. This paper provides an overview of NEML2’s architecture, core features, and applications, highlighting its impact on accelerating material qualification and advancing computational methods in materials science.

GPU

A GPU‐Accelerated Generative Adversarial Model for Causal Inference

We develop a GPU-accelerated machine learning generative adversarial model designed to facilitate causal inferences from observational data. Our model's theoretical framework is conceptualized in a manner that is amenable to being operable and scalable for high-performance computing platforms. We leverage GPU acceleration to develop a parallel evolutionary algorithm to achieve large-scale parallel computation of the model within a now widely accessible computing platform. This capability both enhances computational speedup and efficiency and also extends the use of the model to a broader range of substantive research domains while maintaining the underlying theoretical properties of the model.

GPU