How well do starlab and nbody compare? II. Hardware and accuracy
Version of Record online: 9 MAR 2012
© 2012 The Authors. Monthly Notices of the Royal Astronomical Society © 2012 RAS
Monthly Notices of the Royal Astronomical Society
Volume 421, Issue 4, pages 3557–3569, April 2012
How to Cite
Anders, P., Baumgardt, H., Gaburov, E. and Portegies Zwart, S. (2012), How well do starlab and nbody compare? II. Hardware and accuracy. Monthly Notices of the Royal Astronomical Society, 421: 3557–3569. doi: 10.1111/j.1365-2966.2012.20581.x
- Issue online: 10 APR 2012
- Version of Record online: 9 MAR 2012
- Accepted 2012 January 17. Received 2012 January 12; in original form 2011 December 12
- methods: data analysis – methods: numerical – methods: statistical;
- open clusters and associations: general – galaxies: star clusters: general
Most recent progress in understanding the dynamical evolution of star clusters relies on direct N-body simulations. Owing to the computational demands, and the desire to model more complex and more massive star clusters, hardware calculational accelerators, such as Gravity Pipe (GRAPE) special-purpose hardware or, more recently, graphics prucessing units (GPUs) are generally utilized. In addition, simulations can be accelerated by adjusting parameters determining the calculation accuracy (i.e. changing the internal simulation time-step used for each star).
We extend our previous thorough comparison of basic quantities as derived from simulations performed either with starlab/kira or nbody6. Here we focus on differences arising from using different hardware accelerations (including the increasingly popular graphic card accelerations/GPUs) and different calculation accuracy settings.
We use the large number of star cluster models (for a fixed stellar mass function, without stellar/binary evolution, primordial binaries, external tidal fields, etc.) already used in the previous paper, evolve them with starlab/kira (and nbody6, where required), analyse them in a consistent way and compare the averaged results quantitatively. For this quantitative comparison, we apply the bootstrap algorithm for functional dependencies developed in our previous study.
In general, we find very high comparability of the simulation results, independent of the computer hardware (including the hardware accelerators) and the N-body code used. For the tested accuracy settings, we find that for reduced accuracy (i.e. time-step at least a factor of 2.5 larger than the standard setting) most simulation results deviate significantly from the results using standard settings. The remaining deviations are comprehensible and explicable.