No abstract

In nowadays we observe that there is more data than that can be effectively analyzed. Organizing this data has become one of the biggest problems in Computer Science. Many algorithms have been proposed for this purpose, highlighting those related to the Data Mining area, specifically the automatic document classification (ADC) algorithms. However, these algorithms are still a computational challenge because of the volume of data that needs to be processed. We found in the literature some proposals related to parallelization on graphics processing units (GPUs) to make these algorithms feasible. Still, most of the available parallel solutions ignore specific ADC challenges, such as high dimensionality and heterogeneity in the representation of the documents. In this context, we here present G-KNN, a GPU-based parallel version of the nearest neighbors algorithm (KNN), one of the most widely used ADC algorithms. In our evaluation using five different document collections, we show that the G-KNN can maintain the same classification effectiveness while increasing the efficiency by up to 12x faster than its sequential version using CPU and up to 3x faster than a CPU-based parallel implementation running with 6 threads. Moreover, our algorithm has a much lower memory consumption, enabling its use with large datasets.