Abstract
Combining traditional Wang-Landau sampling for multiple replica systems with an exchange of densities of states between replicas, we describe a general framework for simulations on massively parallel Petaflop supercomputers. The advantages and general applicability of the method for simulations of complex systems are demonstrated for the classical 2D Potts spin model featuring a strong first-order transition and the self-assembly of lipid bilayers in amphiphilic solutions in a continuous model.
Highlights
In recent years the goal of Petaflop computing has been achieved by replying on massively parallel systems. This development requires a new approach to the efficient utilization of computing resources; improvements in methodology in computational statistical physics have taken place
In Wang–Landau (WL) sampling, the a priori unknown density of states g(E) of a system is determined iteratively by performing a random walk in energy space (E) and sampling configurations with probability 1/g(E) [1, 2, 3, 4]. This procedure has proven very powerful for studying wide ranging problems with complex free energy landscapes because it circumvents the long time scales typically encountered near phase transitions or at low temperatures
The method facilitates the calculation of thermodynamic quantities, including the free energy, at any temperature from a single simulation
Summary
In recent years the goal of Petaflop computing has been achieved by replying on massively parallel systems This development requires a new approach to the efficient utilization of computing resources; improvements in methodology in computational statistical physics have taken place. In Wang–Landau (WL) sampling, the a priori unknown density of states g(E) of a system is determined iteratively by performing a random walk in energy space (E) and sampling configurations with probability 1/g(E) (i.e. with a “flat histogram”) [1, 2, 3, 4] This procedure has proven very powerful for studying wide ranging problems with complex free energy landscapes because it circumvents the long time scales typically encountered near phase transitions or at low temperatures.
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