High Performance Direct Gravitational N-body Simulations on Graphics Processing Units
Simon Portegies Zwart, Robert Belleman, Peter Geldof

TL;DR
This paper demonstrates that modern graphics processing units (GPUs) can effectively perform direct gravitational N-body simulations, offering a cost-effective alternative to specialized hardware like GRAPE, with comparable accuracy and scalability for large particle numbers.
Contribution
It introduces a GPU-based implementation of direct N-body simulations, showing competitive performance and accuracy compared to GRAPE hardware, and highlights the advantages of GPU memory capacity and cost.
Findings
GPUs provide an attractive low-cost alternative to GRAPE hardware for large N-body simulations.
GPU simulations conserve total energy within a factor of 10^-6, close to GRAPE performance.
GeForce 8800GTX outperforms host computer by about 20 times for N > 10^6.
Abstract
We present the results of gravitational direct -body simulations using the commercial graphics processing units (GPU) NVIDIA Quadro FX1400 and GeForce 8800GTX, and compare the results with GRAPE-6Af special purpose hardware. The force evaluation of the -body problem was implemented in Cg using the GPU directly to speed-up the calculations. The integration of the equations of motions were, running on the host computer, implemented in C using the 4th order predictor-corrector Hermite integrator with block time steps. We find that for a large number of particles () modern graphics processing units offer an attractive low cost alternative to GRAPE special purpose hardware. A modern GPU continues to give a relatively flat scaling with the number of particles, comparable to that of the GRAPE. Using the same time step criterion the total energy of the -body system was…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
