In control theory, Quantitative Feedback Theory (QFT) developed by Issac Horowitz, has gained a lot of popularity. Many researchers have proposed a method to generate the bounds that from the literature it is observed that generation of bound takes a lot of time for online design of the controller. It is necessary to speed up the computation of bound generation. This paper exhibits the parallel computing power of the GPU (Graphics Processing Unit) in the area of QFT. In this paper, GPU based approach is proposed to speed up the computation of stability bound. By using MATLAB parallel computing toolboxes, GPU computational power can be easily accessed with the minimum knowledge of GPU architecture, MATLAB code can be executed on the GPU. In order to achieve faster execution of QFT bound generation, NVIDIA GPU with the support of MATLAB parallel computing toolbox is used in this work. Performance comparison of the algorithm for sequential implementation on CPU and parallel implementation on GPU is carried out. This work analyzes the relative performance of GPU vs CPU. In this paper, GPU based approach proposed for significant speedup in the computation of bound using QFT and it is observed that GPU provides speedup two to three times as compared to the CPU.

Pallavi d Pawar, Mukesh D Patil, Vishwesh A. Vyawahare (2018). Speed Enhancement of QFT Bound Generation using GPU. International Journal of Computer Engineering In Research Trends, 5(4), 129-135. Retrieved from http://ijcert.org/ems/ijcert_papers/V5I406.pdf

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