High-performance computing simulations of self-gravity in astronomical agglomerates. (March 2023)

• Record Type: Journal Article
• Authors: Rocchetti, Néstor; Nesmachnow, Sergio; Tancredi, Gonzalo
• Is Part Of: Simulation. Volume 99:Number 3(2023)
• Page Start: 263
• Page End: 289
• Publication Date: 2023-03
• Subjects: Simulation -- high-performance computing -- self-gravity -- astronomical agglomerates; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549721998766
• Languages: English
• ISSNs: 0037-5497

Abstract

This article describes the advances in the design, implementation, and evaluation of efficient algorithms for self-gravity simulations in astronomical agglomerates. Three algorithms are presented and evaluated: the occupied cells method, and two variations of the Barnes–Hut method using an octal and a binary tree. Two scenarios are considered in the evaluation: two agglomerates orbiting each other and a collapsing cube. The results show that the proposed octal tree Barnes–Hut method allows improving the performance of the self-gravity calculation up to 100 times with respect to the occupied cells method, while having good numerical accuracy. The proposed algorithms are efficient and accurate methods for self-gravity simulations in astronomical agglomerates.