Wavelets and wavelet transforms can be applied to various problems concerning signals. The ability to transform the signal into something representing frequencies and to see when the frequencies occurred, can be used in numerous fields. The calculation can be computationally expensive when applied to large datasets. By taking advantage of the computational power of a GPU when implementing a wavelet transform, the time of the computation can be substantially reduced. The goal is to make the application fast enough to solve a problem interactively. This thesis introduces the wavelet transform and addresses differences between some GPU toolkits, looking at development and code efficiency.