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Efficient algorithms for large-scale quantum transport calculations.

Sascha BrückMauro CalderaraMohammad Hossein Bani-HashemianJoost VandeVondeleMathieu Luisier
Published in: The Journal of chemical physics (2018)
Massively parallel algorithms are presented in this paper to reduce the computational burden associated with quantum transport simulations from first-principles. The power of modern hybrid computer architectures is harvested in order to determine the open boundary conditions that connect the simulation domain with its environment and to solve the resulting Schrödinger equation. While the former operation takes the form of an eigenvalue problem that is solved by a contour integration technique on the available central processing units (CPUs), the latter can be cast into a linear system of equations that is simultaneously processed by SplitSolve, a two-step algorithm, on general-purpose graphics processing units (GPUs). A significant decrease of the computational time by up to two orders of magnitude is obtained as compared to standard solution methods.
Keyphrases
  • molecular dynamics
  • machine learning
  • deep learning
  • monte carlo
  • density functional theory
  • minimally invasive
  • energy transfer
  • risk factors