Recent years have experienced a slowdown in the development of traditional systems that use only the Central Processing Unit (CPU). However, significant progress has been made in the development of heterogeneous systems utilizing not only the CPU but also the Graphics Processing Unit (GPU). NVIDIA, one of the GPU manufacturers, through its CUDA platform, has increased the interest of many researchers in heterogeneous systems by providing a means to program GPUs more easily. The ease of application development provided by the CUDA platform and the performance gains offered by these heterogeneous systems have encouraged many researchers to develop algorithms and applications that operate on these systems. One such algorithm that is frequently used in data analysis is the community detection algorithm. Although there are applications that implement this algorithm to run on GPUs, and while these applications work efficiently for many datasets, they either fail to work or experience significant performance loss for large datasets that exceed the GPU’s memory capacity. In this thesis, we analyzed Rundemanen, which is one of the community detection applications running on GPU. We also made enhancements that enable Rundemanen to process datasets larger than the GPU’s memory capacity by utilizing CUDA’s Unified Memory. Lastly, we tested various optimization methods to use Unified Memory more efficiently. By using our memory-access advises, in comparison to the naive version, we obtained up to 62x and 8x performance gain with artificial oversubscription scenarios and for datasets that already do not fit into GPU memory, respectively.