Integrating Post-Newtonian Equations on Graphics Processing Units

TL;DR

This paper presents a GPU-accelerated method for simulating binary black hole inspirals using post-Newtonian equations, revealing statistical properties of spins before merger and achieving significant computational speed-up.

Contribution

It introduces a GPU-based implementation of post-Newtonian binary black hole simulations, enabling faster computations and detailed statistical analysis of spin dynamics.

Findings

Black hole spin dot products are uniformly distributed before merger.

High correlation between initial and final spin dot products in equal-mass, maximally spinning cases.

GPU implementation achieves 50x speed-up over CPU methods.

Abstract

We report on early results of a numerical and statistical study of binary black hole inspirals. The two black holes are evolved using post-Newtonian approximations starting with initially randomly distributed spin vectors. We characterize certain aspects of the distribution shortly before merger. In particular we note the uniform distribution of black hole spin vector dot products shortly before merger and a high correlation between the initial and final black hole spin vector dot products in the equal-mass, maximally spinning case. These simulations were performed on Graphics Processing Units, and we demonstrate a speed-up of a factor 50 over a more conventional CPU implementation.