Abstract
While the use of enhanced sampling techniques and parallel computing to determine potentials of mean force is in widespread use in modern Molecular Dynamics and Monte Carlo simulation studies, there have been few methods that efficiently combine heterogeneous computer resources of varying quality and speeds in realizing a single simulation result on a distributed network. Here, we apply an algorithm based on the Monte Carlo method of Wang and Landau within a client-server framework, in which individual computing nodes report a histogram of regions of phase space visited and corresponding updates to a centralized server at regular intervals entirely asynchronously. The server combines the data and reports the sum to all nodes so that the overall free energy determination scales linearly with the total amount of resources allocated. We discuss our development of this technique and present results for molecular simulations of DNA.
Keywords
- Computing Node
- Monte Carlo Simulation Study
- Free Energy Simulation
- Nucleoid Protein
- Standard Monte Carlo Simulation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Czapla, L., Siretskiy, A., Grime, J., Khan, M.O. (2012). Free Energy Monte Carlo Simulations on a Distributed Network. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28145-7_1
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DOI: https://doi.org/10.1007/978-3-642-28145-7_1
Publisher Name: Springer, Berlin, Heidelberg
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