Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHow well do STARLAB and NBODY compare? II: Hardware and accuracy
120
0
0.0
(
0
)
Authors
P. Anders
Ask ChatGPT about the research
No Arabic abstract
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 GRAPE special-purpose hardware or, more recently, GPUs (i.e. graphics cards), are generally utilised. 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 (Anders et al. 2009) 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 used computer hardware (including the hardware accelerators) and the used N-body code. For the tested accuracy settings we find that for reduced accuracy (i.e. time step at least a factor 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.
rate research
Read More
N-body simulations are widely used to simulate the dynamical evolution of a variety of systems, among them star clusters. Much of our understanding of their evolution rests on the results of such direct N-body simulations. They provide insight in the structural evolution of star clusters, as well as into the occurrence of stellar exotica. Although the major pure N-body codes STARLAB/KIRA and NBODY4 are widely used for a range of applications, there is no thorough comparison study yet. Here we thoroughly compare basic quantities as derived from simulations performed either with STARLAB/KIRA or NBODY4. We construct a large number of star cluster models for various stellar mass function settings (but without stellar/binary evolution, primordial binaries, external tidal fields etc), evolve them in parallel with STARLAB/KIRA and NBODY4, analyse them in a consistent way and compare the averaged results quantitatively. For this quantitative comparison we develop a bootstrap algorithm for functional dependencies. We find an overall excellent agreement between the codes, both for the clusters structural and energy parameters as well as for the properties of the dynamically created binaries. However, we identify small differences, like in the energy conservation before core collapse and the energies of escaping stars, which deserve further studies. Our results reassure the comparability and the possibility to combine results from these two major N-body codes, at least for the purely dynamical models (i.e. without stellar/binary evolution) we performed. (abridged)
We present GAMER-2, a GPU-accelerated adaptive mesh refinement (AMR) code for astrophysics. It provides a rich set of features, including adaptive time-stepping, several hydrodynamic schemes, magnetohydrodynamics, self-gravity, particles, star formation, chemistry and radiative processes with GRACKLE, data analysis with yt, and memory pool for efficient object allocation. GAMER-2 is fully bitwise reproducible. For the performance optimization, it adopts hybrid OpenMP/MPI/GPU parallelization and utilizes overlapping CPU computation, GPU computation, and CPU-GPU communication. Load balancing is achieved using a Hilbert space-filling curve on a level-by-level basis without the need to duplicate the entire AMR hierarchy on each MPI process. To provide convincing demonstrations of the accuracy and performance of GAMER-2, we directly compare with Enzo on isolated disk galaxy simulations and with FLASH on galaxy cluster merger simulations. We show that the physical results obtained by different codes are in very good agreement, and GAMER-2 outperforms Enzo and FLASH by nearly one and two orders of magnitude, respectively, on the Blue Waters supercomputers using $1-256$ nodes. More importantly, GAMER-2 exhibits similar or even better parallel scalability compared to the other two codes. We also demonstrate good weak and strong scaling using up to 4096 GPUs and 65,536 CPU cores, and achieve a uniform resolution as high as $10{,}240^3$ cells. Furthermore, GAMER-2 can be adopted as an AMR+GPUs framework and has been extensively used for the wave dark matter ($psi$DM) simulations. GAMER-2 is open source (available at https://github.com/gamer-project/gamer) and new contributions are welcome.
We give an overview about equations of state (EOS) which are currently available for simulations of core-collapse supernovae and neutron star mergers. A few selected important aspects of the EOS, such as the symmetry energy, the maximum mass of neutron stars, and cluster formation, are confronted with constraints from experiments and astrophysical observations. There are just very few models which are compatible even with this very restricted set of constraints. These remaining models illustrate the uncertainty of the uniform nuclear matter EOS at high densities. In addition, at finite temperatures the medium modifications of nuclear clusters represent a conceptual challenge. In conclusion, there has been significant development in the recent years, but there is still need for further improved general purpose EOS tables.
The SDSS-IV Mapping Nearby Galaxies at APO (MaNGA) program has been operating from 2014-2020, and has now observed a sample of 9,269 galaxies in the low redshift universe (z ~ 0.05) with integral-field spectroscopy. With rest-optical (lambdalambda 0.36 - 1.0 um) spectral resolution R ~ 2000 the instrumental spectral line-spread function (LSF) typically has 1sigma width of about 70 km/s, which poses a challenge for the study of the typically 20-30 km/s velocity dispersion of the ionized gas in present-day disk galaxies. In this contribution, we present a major revision of the MaNGA data pipeline architecture, focusing particularly on a variety of factors impacting the effective LSF (e.g., undersampling, spectral rectification, and data cube construction). Through comparison with external assessments of the MaNGA data provided by substantially higher-resolution R ~ 10,000 instruments we demonstrate that the revised MPL-10 pipeline measures the instrumental line spread function sufficiently accurately (<= 0.6% systematic, 2% random around the wavelength of Halpha) that it enables reliable measurements of astrophysical velocity dispersions sigma_Halpha ~ 20 km/s for spaxels with emission lines detected at SNR > 50. Velocity dispersions derived from [O II], Hbeta, [O III], [N II], and [S II] are consistent with those derived from Halpha to within about 2% at sigma_Halpha > 30 km/s. Although the impact of these changes to the estimated LSF will be minimal at velocity dispersions greater than about 100 km/s, scientific results from previous data releases that are based on dispersions far below the instrumental resolution should be reevaulated.
Cosmological simulations of galaxy formation often rely on prescriptions for star formation and feedback that depend on halo properties such as halo mass, central over-density, and virial temperature. In this paper we address the convergence of individual halo properties, based on their number of particles N, focusing in particular on the mass of halos near the resolution limit of a simulation. While it has been established that the halo mass function is sampled on average down to N~30 particles, we show that individual halo properties exhibit significant scatter, and some systematic biases, as one approaches the resolution limit. We carry out a series of cosmological simulations using the Gadget2 and Enzo codes with N_p=64^3 to N_p=1024^3 total particles, keeping the same large-scale structure in the simulation box. We consider boxes from l_{box} = 8 Mpc/h to l_{box} = 512 Mpc/h to probe different halo masses and formation redshifts. We cross-identify dark matter halos in boxes at different resolutions and measure the scatter in their properties. The uncertainty in the mass of single halos depends on the number of particles (scaling approximately as N^{-1/3}), but the rarer the density peak, the more robust its identification. The virial radius of halos is very stable and can be measured without bias for halos with N>30. In contrast, the average density within a sphere containing 25% of the total halo mass is severely underestimated (by more than a factor 2) and the halo spin is moderately overestimated for N<100. If sub-grid physics is implemented upon a cosmological simulation, we recommend that rare halos (~3sigma peaks) be resolved with N>100 particles and common halos (~1sigma peaks) with N>400 particles to avoid excessive numerical noise and possible systematic biases in the results.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
