By Alexander Heinecke, Wolfgang Eckhardt, Martin Horsch, Hans-Joachim Bungartz
This paintings provides sleek implementations of suitable molecular dynamics algorithms utilizing ls1 mardyn, a simulation software for engineering purposes. The textual content focuses strictly on HPC-related elements, masking implementation on HPC architectures, taking Intel Xeon and Intel Xeon Phi clusters as representatives of present systems. The paintings describes disbursed and shared-memory parallelization on those systems, together with load balancing, with a selected specialize in the effective implementation of the compute kernels. The textual content additionally discusses the software-architecture of the ensuing code.
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Rüde (SCS European Publishing House, Erlangen, 2005), pp. 117–123 21. G. Sutmann, V. Stegailov, Optimization of neighbor list techniques in liquid matter simulations. J. Mol. Liq. 125, 197–203 (2006) 22. M. Buchholz, Framework zur Parallelisierung von Molekulardynamiksimulationen in verfahrenstechnischen Anwendungen. Dissertation, Institut für Informatik, Technische Universität München (2010) 23. P. Gonnet, A simple algorithm to accelerate the computation of non-bonded interactions in cell-based molecular dynamics simulations.
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1) Cost A = AreaCost A + SepCost A . The first one is the computation cost associated with an area AreaCost A . Let Neighbor(i) denote all neighboring cells of a cell i and Ni is the number of particles in a cell. Then the computational cost of an area A is ⎛ ⎝ Ni2 + AreaCost A = i∈Area A j∈Neighbors(i) ⎞ 1 Ni N j ⎠ . 2 The second part of Eq. 1) is the cost caused by the separation, SepCost, because the interactions between cells of different subdomains have to be computed twice, once for each neighboring process.