Improved Ways to Compare Simulations to Data

Theoretical models for structure formation with Gaussian initial fluctuations have been worked out in considerable detail and compared with observations on various scales. It is on nonlinear scales \lsim 10 \ h^{-1}\ {\rm Mpc} that the greatest differences exist between $\Omega=1$ models that have been normalized to agree on the largest scales with the COBE data; here especially there is a need for better statistical tests which are simultaneously {\it robust}, {\it discriminatory}, and {\it interpretable}. The era at which galaxy and cluster formation occurs is also a critical test of some models. Needs for the future include faster and cleverer codes, better control of cosmic variance in simulations, better understanding of processes leading to galaxy formation, better ways of comparing observational data with models, and better access to observational and simulation data.Comment: 9 pages, self-extracting uuencoded postscript, encoded with uufile

Accelerating Dust Temperature Calculations with Graphics Processing Units

When calculating the infrared spectral energy distributions (SEDs) of galaxies in radiation-transfer models, the calculation of dust grain temperatures is generally the most time-consuming part of the calculation. Because of its highly parallel nature, this calculation is perfectly suited for massively parallel general-purpose Graphics Processing Units (GPUs). This paper presents an implementation of the calculation of dust grain equilibrium temperatures on GPUs in the Monte-Carlo radiation transfer code Sunrise, using the CUDA API. The GPU can perform this calculation 69 times faster than the 8 CPU cores, showing great potential for accelerating calculations of galaxy SEDs.Comment: 7 pages, 2 figures, accepted to New Astronomy. Minor updates to text and performance based on feedback from refere