Accelerating common machine learning algorithms through GPGPU symbolic computing

This paper evaluates the implementation of two well known machine learning algorithms, kernel k-means and logistic regression, using Graphics Processing Units (GPUs). The main goal was to do an implementation that exploited the processing power of GPU while keeping the implementation simple, easy to understand and modify. The paper presents an empirical analysis of the performance of the implementations under different execution scenarios.