  The present research builds on a recently proposed spatial prediction method
for discretized two-dimensional data, based on a suitably modified planar
rotator (MPR) spin model from statistical physics. This approach maps the
measured data onto interacting spins and, exploiting spatial correlations
between them, which are similar to those present in geostatistical data,
predicts the data at unmeasured locations. Due to the short-range nature of the
spin pair interactions in the MPR model, parallel implementation of the
prediction algorithm on graphical processing units (GPUs) is a natural way of
increasing its efficiency. In this work we study the effects of reduced
computing precision as well as GPU-based hardware intrinsic functions on the
speedup and accuracy of the MPR-based prediction and explore which aspects of
the simulation can potentially benefit the most from the reduced precision. It
is found that, particularly for massive data sets, a thoughtful precision
setting of the GPU implementation can significantly increase the computational
efficiency, while incurring little to no degradation in the prediction
accuracy.
