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A conventional genetic algorithm occasionally has limitations due to a low convergence rate in spite of high computing times.
This combination of high computing capability and density scalability can be obtained with the nanodevice technology, notably by resistive-switching memory (RRAM) devices.
This has become possible due to a confluence of dramatic progress in microscope engineering, enabling efficient image collection, and the availability of high computing power for data analysis.
In view of high computing cost, special emphasis is placed on effective program design, such as application of advanced detection algorithm for particle particle/wall collisions, the multi-grid method and parallel calculation.
Dense marker maps have become state of the art and aggravate the problem of high computing times required for Bayesian approaches.
Systems biology and personalized medicine have become a fast growing field and have been more and more advanced especially in the light of high computing power, low cost sequencing opportunities and complex networks, underlying disease pathologies.
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Compared with homogenization method, the strain energy-based method has advantages of higher computing efficiency and simplified programming.
As demand of higher computing power is steadily increasing, it becomes popular to equip a many-core accelerator in a computer system to run concurrent applications.
In the future, some studies about the allocation of virtual machines, task scheduling and load balancing strategy are hoped to be carried out to meet the needs of higher computing performance and analysis efficiency when applied to power flow calculation.
The effect of higher computing cost in OMP-PKS+RS can be reduced by parallel processing, because the reconstruction of each signal in OMP-PKS+RS can be done separately.
However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms.
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