Modern graphics processing units (GPUs) containing massive parallel hardware have become more flexible with unified shader cores which can run diverse graphics operations. Moreover, programmers can run general-purpose applications on GPUs easily, since GPU vendors provide user-friendly application programming interfaces (APIs). Many studies for improving system performance using GPUs have been researched intensively. Study on the GPU architecture is challenging, because the GPU architecture is totally different to the traditional CPU architecture. This paper analyzes the GPU performance according to GPU parameters with various number of cores and clock frequency. According to our simulations, the GPU performance improves by 125.8% and 16.2% on average as the number of cores and clock frequency increase, respectively. However, the performance is saturated when memory system cannot service the data requests efficiently, resulting in memory bottleneck. Consequently, memory bottleneck problem should be considered for efficient GPU architecture design.