Over the last few years, we have witnessed the proliferation of GPU devices on HPC environments. Manufacturers produce new versions of their devices every few years, though, posing a new problem for scientists and engineers using their technology: is it worth the time and effort spent optimizing the codes for the current version? Or it is better to wait until a new architecture appears? In this paper, we present a comparison of various CUDA versions, in order to compare their architectures, and optimize codes for each version. This work would require a tremendous coding effort if done manually. However, using fast prototyping tools, like llCoMP, this is an effortless process. Applying loop optimization techniques, we evaluate three different algorithms. With each one, we apply a set of optimization techniques, showing the performance benefit or penalty, in three CUDA architecture versions, including Fermi. The results of these techniques will guide developers on the right path towards efficient code optimization. Preliminary results show that some optimizations recommended for older CUDA architectures may not be useful in Fermi.