Fast CUDA-based Implementations of Automatic Document Classification Algorithms - Ramos, Gabriel; Andrade, Guilherme; Viegas, Felipe; Madeira, Daniel; Rocha, Leonardo (Proceedings of the SBC Undergraduate Research Contest (CTIC-SBC), 2014)
With the advent of Web 2.0, we see a new scenario: there is more data available than that can be effectively analyzed. Organizing this data is one of the biggest problems in Computer Science. Many algorithms have been proposed for this purpose, highlighting those related to Automatic Document Classification (ADC). There are some proposals to make these algorithms feasible and those related to parallelization on graphics processing units (GPUs) have presented good results. In this work we present two GPU parallel version of ADC algorithms, the GPU-NB, based on Naïve Bayes, and G-KNN, based on KNN. We show that, besides achieved the same effectiveness, our approaches were faster than their CPU versions.