The GCA (Global Cellular Automata) model is a very interesting and flexible model which can be used to implement all kind of parallel algorithms. The GCA model consists of a field of cells similar the Cellular Automata model. Each cell has links to a set of remote cells which can be dynamically changed from generation to generation. A cell reads the remote neighbors' states and then changes its own state according to a local rule. The model is massively parallel because all cells can change their states independently and in parallel. We have investigated how the GCA model can be implemented efficiently in hardware using a Field Programmable Gate Array (FPGA) prototyping platform. We have implemented a fully parallel architecture where all cells operate fully in parallel and other architectures where the cells are stored in memories in order to handle a large number of cells. We are showing that in the fully parallel architecture a speed-up of around 190 is realistic on a modern FPGA platform compared to a software implementation on a PC. In the partially parallel architecture based on memories the speed-up will be lower but the number of cells is only restricted by the capacity of the memories.