This thesis demonstrates the advantages of new practical implementations of compressive sensing (CS) algorithms tailored for the graphics processing unit (CPU) using a software platform called Jacket. There exist many applications which utilize CS including medical imaging, signal processing and data acquisition which have benefited from advancements in CS. However, as problems become larger not only do they become more difficult to solve but also more computationally expensive. In light of tins, existing CS algorithms are augmented for practical use on the CPU, reaping performance gains from the highly parallel architecture of the GPU. I discuss the issues associated with this transition and analyze the effects of such a movement, as well as provide results exhibiting advantages of using CPU-based methods.