Stochastic Rotation Dynamics (SRD) is a particle-based simulation method that can be used to model complex fluids either in two or three dimensions, which is very useful in biology and physics study. Although SRD is computationally efficient compared to other simulations, it still takes a long time to run the simulation when the size of the model is large, e.g. when using a large array of particles to simulate dense polymers. In some cases, the simulation could take months before getting the results. Thus, this research focuses on the acceleration of the SRD simulation by using GPU. GPU acceleration can reduce the simulation time by orders of magnitude. It is also cost-effective because a GPU costs significantly less than a computer cluster. Compute Unified Device Architecture (CUDA) programming makes it possible to parallelize the program to run on hundreds or thousands of thread processors on GPU. The program is divided into many concurrent threads. In addition, several kernel functions are used for data synchronization. The speedup of GPU acceleration is varied for different parameters of the simulation program, such as size of the model, density of the particles, formation of polymers, and above all the complexity of the algorithm itself. Compared to the CPU version, it is about 10 times speedup for the particle simulation and up to 50 times speedup for polymers. Further performance improvement can be achieved by using multiple GPUs and code optimization.