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It has been found that the collision model not only dramatically decreases computing time compared to the direct collision method, but also provides an excellent computational efficiency on parallel platforms.
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To decrease computing time and resources, a two-dimensional to three-dimensional global local finite element numerical method is presented, in which both the local zone selection strategy and boundary constraints applying method are discussed in detail.
Parallel processing using multi-threads on multi-core processors is a standard approach to decrease computing time for the calculation of local LSPs based on moving window operations (e.g. slope, curvature).
To decrease computing time of collision detection, it is easy and useful to decrease number of source nodes, however it may lead to false collision detection and increase effects of the overhead.
This shows that our surrogate family approximation can decrease computing time without losing accuracy.
The starting value used is not so important when estimating the results, but setting it to a value close to the actual solution (using prior knowledge or literature results) might decrease computing time.
Whereas character-state data (e.g., nucleotide sequences) are commonly used for parsimony, maximum likelihood or Bayesian analyses, distance methods can be selected as an alternative option to decrease computing time when analysing large data sets, or else, can be used in comparative studies where the primary data are not available.
An Apache Hadoop cloud computing platform was implemented for decreasing computation time, and pathway information from the KEGG was applied for discovering meaningful biological correlations.
Using quasi-1D significantlyicantly decreases overall computing time and its results are in good agreement with 3D Navier Stokes equations solver for these types of application.
Low source nodes contribute to decrease in computing time of collision detection.
In order to decrease the computing time, the number of source nodes is reduced.
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