  We developed a CUDA-based parallelization of the annealing method for the
inverse Laplace transform problem. The algorithm is based on annealing
algorithm and minimizes residue of the reconstruction of the spectral function.
We introduce local updates which preserve first two sum rules and allow an
efficient parallel CUDA implementation. Annealing is performed with the Monte
Carlo method on a population of Markov walkers. We propose imprinted branching
method to improve further the convergence of the anneal. The algorithm is
tested on truncated double-peak Lorentzian spectrum with examples of how the
error in the input data affects the reconstruction.
