We present a massively parallel algorithm for calculating the self-energy in self-consistent finite temperature perturbation theory for lattice models. The algorithm uses analytic functions with appropriate asymptotic high frequency behavior and fast Fourier transforms to accurately calculate the self-energy at low-frequency. Traditional methods that truncate the high frequency tails of the temperature Green's function lead to `contamination' of the low-frequency behavior of the self-energy. Our algorithm is both accurate and scalable. We compare results for the Hubbard model using various techniques for handling the high frequency tails of the temperature Green's function.
We study the spectral function of the two-dimensional Hubbard model using cluster perturbation theory, and a density matrix renormalization group as a cluster solver. We reconstruct the two-dimensional dispersion at and away from half-filling using $2\ifmmode\times\else\texttimes\fi{}L$ ladders, with
L up to 80 sites, yielding results with unprecedented resolution in excellent agreement with quantum Monte Carlo. The main features of the spectrum can be described with a mean-field dispersion, with kinks and pseudogap traced back to scattering between spin and charge degrees of freedom.