Acceleration of information-theoretic data analysis with graphics processing units

Information-theoretic measures are frequently employed to assess the degree of feature interactions when mining attribute-value data sets. For large data sets, obtaining these measures quickly poses an unmanageable computational burden. In this work we examine the applicability of consumer graphics processing units supporting CUDA architecture to speed-up the computation of information-theoretic measures. Our implementation was tested on a variety of data sets, and compared with the performance of sequential algorithms running on the central processing unit.