high performance computing on graphics processing units: hgpu.org

With continually increasing data sizes, the relevance of the big n problem of classical likelihood approaches is greater than ever. This paper considers functional data, and presents operator approximations, where observations are embedded in function space, and likelihood calculations are carried out in the functional domain. The resulting approximated problems are naturally parallel and can be solved in linear time. An extremely efficient GPU implementation is presented, and the proposed methods are illustrated by conducting a classical statistical analysis of a dataset of 2D chromatograms consisting of more than 140 million spatially correlated observation points.