Several demands relied on high-performance computing come up with the advanced technologies, like Satellite Imaging, Genetic Engineering, Global Weather Forecast, Nuclear Explosion Emulation, and in the meantime, the amount of data usually approaches the rank of Tera-Bytes, even Peta-Bytes. Besides, we need practical image application in our daily life, such as Game, 3D Display, High-Definition Video, etc. These requirements of high-performance computing are rigorous challenge to current devices. The performance of GPU (Graphic Processing Unit) is growing up rapidly in recent years. GPU doubles its computing power every year, which is far superior to CPU (Central Processing Unit) performance based on Moore¡¦s Law. Nowadays, the computing power of GPU on the single-precision floating-point operations is ten times than that of CPU. Furthermore, CUDA (Compute Unified Device Architecture) is a parallel computing architecture proposed by NVIDIA at 2007, and it is the first C-like language software development environment without Graphics API. In this research, we use GPU to assist network devices in filtering packets of the network flow, whose quantity is becoming more and more large. Due to the popularization of network, people pay attention to different types of network attacks or safety problems. Therefore, it is important to remove malicious packets from normal ones without degrading the network performance.

The diploma thesis deal with a priority network switch whose model was made in programming environment Matlab - Simulink. Problem of optimal switching is solved by Hopfield’s artificial neural network. Produce of the diploma thesis is a model of packet switch and time-severity comparison of optimalization problem solved with or without artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.