With the advent of Web 2.0, we see a new and differentiated scenario: 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 clustering 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 to make these algorithms feasible, and, recently, those related to parallelization on graphics processing units (GPUs) have presented good results. In this work we present the G-DBSCAN, a GPU parallel version of one of the most widely used clustering algorithms, the DBSCAN. Although there are other parallel versions of this algorithm, our technique distinguishes itself by the simplicity with which the data are indexed, using graphs, allowing various parallelization opportunities to be explored. In our evaluation we show that the G-DBSCAN using GPU, can be over 100x faster than its sequential version using CPU.
Furthermore, REF presented a GPU implementation of DBSCAN that can be over 100x faster than sequential DBSCAN, a speedup our algorithm has also been seen to achieve but using only a single processor.
G-DBSCAN: A GPU Accelerated Algorithm for Density-based Clustering
ICCS
Computer Science