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

SAIL: A CUDA-based implementation of the simulated annealing for the inverse Laplace transform problem