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\section{\label{sec:ProgramDesign}Program Design} |
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\subsection{\label{sec:architecture} OOPSE Architecture} |
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\subsection{\label{sec:programLang} Programming Languages } |
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\subsection{\label{sec:parallelization} Parallelization of OOPSE} |
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Although processor power is doubling roughly every 18 months according |
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to the famous Moore's Law\cite{moore}, it is still unreasonable to |
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simulate systems of more then a 1000 atoms on a single processor. To |
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facilitate study of larger system sizes or smaller systems on long |
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time scales in a reasonable period of time, parallel methods were |
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developed allowing multiple CPU's to share the simulation |
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workload. Three general categories of parallel decomposition method's |
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have been developed including atomic, spatial and force decomposition |
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methods. |
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Algorithmically simplest of the three method's is atomic decomposition |
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where N particles in a simulation are split among P processors for the |
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duration of the simulation. Computational cost scales as an optimal |
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$O(N/P)$ for atomic decomposition. Unfortunately all processors must |
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communicate positions and forces with all other processors leading |
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communication to scale as an unfavorable $O(N)$ independent of the |
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number of processors. This communication bottleneck led to the |
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development of spatial and force decomposition methods in which |
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communication among processors scales much more favorably. Spatial or |
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domain decomposition divides the physical spatial domain into 3D boxes |
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in which each processor is responsible for calculation of forces and |
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positions of particles located in its box. Particles are reassigned to |
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different processors as they move through simulation space. To |
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calculate forces on a given particle, a processor must know the |
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positions of particles within some cutoff radius located on nearby |
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processors instead of the positions of particles on all |
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processors. Both communication between processors and computation |
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scale as $O(N/P)$ in the spatial method. However, spatial |
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decomposition adds algorithmic complexity to the simulation code and |
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is not very efficient for small N since the overall communication |
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scales as the surface to volume ratio $(N/P)^{2/3}$ in three |
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dimensions. |
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Force decomposition assigns particles to processors based on a block |
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decomposition of the force matrix. Processors are split into a |
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optimally square grid forming row and column processor groups. Forces |
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are calculated on particles in a given row by particles located in |
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that processors column assignment. Force decomposition is less complex |
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to implement then the spatial method but still scales computationally |
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as $O(N/P)$ and scales as $(N/\sqrt{p})$ in communication |
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cost. Plimpton also found that force decompositions scales more |
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favorably then spatial decomposition up to 10,000 atoms and favorably |
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competes with spatial methods for up to 100,000 atoms. |
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\subsection{\label{sec:memory}Memory Allocation in Analysis} |
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\subsection{\label{sec:documentation}Documentation} |
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\subsection{\label{openSource}Open Source and Distribution License} |
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