The massive parallelism provided by the modern graphics processing units (GPUs) makes them the attractive processors to accelerate the applications with high data-level parallelism. Therefore, the GPU architecture has recently gained a lot of attention in research community. However, the advance in the GPU architecture is impeded by the limited documents released from the major GPU vendors. Furthermore, current studies on GPUs often focus only on general-purpose (GPGPU) applications. The behaviors of the graphics applications, which are considered as the major GPU workloads, are often overlooked in these studies. A GPU design good for the GPGPU applications is not necessarily good for the graphics applications. Therefore, a simulation framework that is able to provide performance characterization for both applications is mandatory for the innovation of the GPU architecture.