Work

RT-BarnesHut: Accelerating Barnes-Hut Using Ray-Tracing Hardware

Year: 2025

Type: conference-paper

Abstract: The n-body problem involves calculating the effect of bodies on each other. n-body simulations are ubiquitous in the fields of physics and astronomy and notoriously computationally expensive. The naïve algorithm for n-body simulations has the prohibiting O(n2) time complexity. Reducing the time complexity to O(n · lg(n)), the tree-based Barnes-Hut algorithm approximates the effect of bodies beyond a certain threshold distance. Other than algorithmic improvements, extensive research has gone into accelerating n-body simulations on GPUs and multi-core systems. However, Barnes-Hut is a tree-traversal algorithm, which makes it a poor target for acceleration using traditional GPU shader cores. In contrast, recent work shows that, for tree-based computations, GPU ray-tracing (RT) cores dominate shader cores. In this work, we reformulate the Barnes-Hut algorithm as a ray-tracing problem and implement it with NVIDIA OptiX. Our evaluation shows that the resulting system, RT-BarnesHut, outperforms current state-of-the-art GPU-based implementations.

Source: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

DOI: 10.1145/3710848.3710885

Authors: Vani Nagarajan, Rohan Gangaraju, Kirshanthan Sundararajah, Artem Pelenitsyn, Milind Kulkarni

Institutions: Purdue University West Lafayette, The University of Texas at Austin, Virginia Tech

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Universitas Brawijaya

Award: G3070990151