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This paper demonstrates Medusa, a programming framework for parallel graph processing on graphics processors (GPUs). Medusa enables developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime system of Medusa automatically executes the user-defined APIs in parallel on the GPU, with a series of graph-centric optimizations based on the architecture features of GPUs. We will demonstrate the steps of developing GPU-based graph processing algorithms with Medusa, and the superior performance of Medusa with both real-world and synthetic datasets.