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For comparison purpose, the speedups were computed using as reference the time spent by the sequential implementation in a host of the computing system were the respective parallel algorithms where executed.
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Specifically, the sequential implementation by Correia at the Biomedical Engineering Institute, Engineering School of the University of Porto [26, 27] has been used.
In this section, we constructed a scalable version of the mining tool for identifying the effective drug combinations and compared its efficiency to that of the sequential implementation by the traditional way.
The speedup achieved by the DWT GPU-based algorithm is up to 20 times relative to the sequential implementation in one core.
In this section, the sequential implementation of the Lucas-Kanade algorithm proposed by Correia [26, 27] is described because this implementation has been used as the starting point for the parallelization.
For the sequential implementation on FPGA platform, the proposed hardware architecture has been implemented on a Xilinx Virtex-6 ML605 FPGA.
Compared to the sequential implementation, we found no influence of the parallelization on simulation results.
However, the remaining sequential algorithm part in the parallel algorithm (zones of interest generation and evolution step) represents a minor part of the processing time in the sequential implementation, thus allowing an efficient parallel implementation according the Amadahl's law.
As estimation, the sequential implementation of a degraded cascade detector can achieve on an average of 10 frames per second.
Results of perceptual experiments show that by integrating the steps of language generation and speech synthesis, we are able to achieve improved naturalness of synthetic speech compared to the sequential implementation.
The sequential implementation of the above two modules manages to significantly reduce the computational cost of the quantizer.
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