Astrophysical Particle Simulations on Heterogeneous CPU-GPU Systems
Naohito Nakasato, Go Ogiya, Yohei Miki, Masao Mori, Ken’ichi Nomoto
arXiv:1206.1199v1 [astro-ph.IM] (6 Jun 2012)
@article{2012arXiv1206.1199N,
author={Nakasato}, N. and {Ogiya}, G. and {Miki}, Y. and {Mori}, M. and {Nomoto}, K.},
title={"{Astrophysical Particle Simulations on Heterogeneous CPU-GPU Systems}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1206.1199},
primaryClass={"astro-ph.IM"},
keywords={Astrophysics – Instrumentation and Methods for Astrophysics, Computer Science – Performance, Physics – Computational Physics},
year={2012},
month={jun},
adsurl={http://adsabs.harvard.edu/abs/2012arXiv1206.1199N},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
A heterogeneous CPU-GPU node is getting popular in HPC clusters. We need to rethink algorithms and optimization techniques for such system depending on the relative performance of CPU vs. GPU. In this paper, we report a performance optimized particle simulation code "OTOO", that is based on the octree method, for heterogenous systems. Main applications of OTOO are astrophysical simulations such as N-body models and the evolution of a violent merger of stars. We propose optimal task split between CPU and GPU where GPU is only used to compute the calculation of the particle force. Also, we describe optimization techniques such as control of the force accuracy, vectorized tree walk, and work partitioning among multiple GPUs. We used OTOO for modeling a merger of two white dwarf stars and found that OTOO is powerful and practical to simulate the fate of the process.
June 8, 2012 by hgpu
