This thesis studies the impact of hardware features of graphics cards on performance of GPU computing using GPGPU-Sim simulation software tool. GPU computing is a growing topic in the world of computing, and could be an important milestone for computers. Therefore, such a study that seeks to identify the performance bottlenecks of the program with respect to hardware parameters of the devvice can be considered an important step towards tuning devices for higher efficiency. In this work we selected convolution algorithm - a typical GPGPU application - and conducted several tests to study different performance parameters. These tests were performed on two simulated graphics cards (NVIDIA GTX480, NVIDIA Tesla C2050), which are supported by GPGPU-Sim. By changing the hardware parameters of graphics card such as memory cache sizes, frequency and the number of cores, we can make a fine-grained analysis on the effect of these parameters on the performance of the program. A graphics card working on a picture convolution task releis on the L1 cache but has the worst performance with a small shared memory. Using this simulator to run performance tests on a theoretical GPU architecture could lead to better GPU design for embedded systems.