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Since our experiments typically generate large amounts of data (over 100 Gb per experiment), we implemented our segmentation and tracking algorithms to analyze each field separately, such that computation time can be shortened by using parallel computing.
While the computational complexity of all neural networks is high, the actual training time is often reduced by using parallel computing resources.
ANN, a humanlike system of nerve structure of brain, is an intelligent method can similarly imitate how the brain works by using parallel computing model.
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Using parallel computing techniques, the computational time is shown to be significantly reduced by increasing the number of processors while maintaining less than 1% error in mass balance.
So the method was difficult on using parallel computing on a computer.
It was built from the ground up as a modular, highly extensible system focused on speed of calculations that is to be achieved by using proper simplification of physical and other effects, and by the ability to perform simulations using parallel computing.
These emerging computational requirements can be met using parallel computing techniques.
Therefore, potential speedup can be achieved using parallel computing hardware.
The performance of SpMV can be improved using parallel computing.
The efficiency of improvement using parallel computing can be roughly estimated using Amdahl's law.
To tackle this issue, we implemented the GN analysis algorithms using parallel computing techniques.
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