SCYON Abstract

Received on September 10 2012

A Parallel Monte Carlo Code for Simulating Collisional N-body Systems

Authors	Bharath Pattabiraman, Stefan Umbreit, Wei-King Liao, Alok Choudhary, Vassiliki Kalogera, Gokhan Memik, and Frederic A. Rasio
Affiliation	Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) & Dept. of Physics and Astronomy, Northwestern University, 2145 Sheridan Rd, Evanston, IL 60208, USA
Submitted to	Astrophysical Journal Supplement Series
Contact	Bharath Pattabiraman
URL	http://arxiv.org/abs/1206.5878
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Abstract

We present a new parallel code for computing the dynamical evolution of collisional N-body systems with up to N~10⁷ particles. Our code is based on the the Henon Monte Carlo method for solving the Fokker-Planck equation, and makes assumptions of spherical symmetry and dynamical equilibrium. The principal algorithmic developments involve optimizing data structures, and the introduction of a parallel random number generation scheme, as well as a parallel sorting algorithm, required to find nearest neighbors for interactions and to compute the gravitational potential. The new algorithms we introduce along with our choice of decomposition scheme minimize communication costs and ensure optimal distribution of data and workload among the processing units. The implementation uses the Message Passing Interface (MPI) library for communication, which makes it portable to many different supercomputing architectures. We validate the code by calculating the evolution of clusters with initial Plummer distribution functions up to core collapse with the number of stars, N, spanning three orders of magnitude, from 10⁵ to 10⁷. We find that our results are in good agreement with self-similar core-collapse solutions, and the core collapse times generally agree with expectations from the literature. Also, we observe good total energy conservation, within less than 1% throughout all simulations. We analyze the performance of the code, and demonstrate near-linear scaling of the runtime with the number of processors up to 64 processors for N=10⁵, 128 for N=10⁶ and 256 for N=10⁷. The runtime reaches a saturation with the addition of more processors beyond these limits which is a characteristic of the parallel sorting algorithm. The resulting maximum speedups we achieve are approximately 60x, 100x, and 220x, respectively.