GPUs represent one of the most sophisticated and versatile parallel
computing architectures that have recently entered in the HEP field.
GooFit is an open source tool interfacing ROOT/RooFit to the CUDA
platform that allows to manipulate probability density functions and
perform fitting tasks. The computing capabilities of GPUs with
respect to traditional CPU cores have been explored with a high-statistics
pseudo-experiment method implemented in GooFit with the purpose
of estimating the local statistical significance of an already known signal.
The striking performance obtained by using GooFit on GPUs has been
discussed in the previous edition (XII) of this conference.
This method has been extended to situations when, dealing with an
unexpected new signal, a global significance must be estimated.
The LEE is taken into account by means of a scanning/clustering technique
in order to consider, within the same background only fluctuation and
anywhere in the relevant mass spectrum, any fluctuating peaking
behaviour with respect to the background model. The presented results
clearly indicate that the systematic uncertainty associated to the method
is negligible and that the p-value estimation is not affected by the clustering
configuration. A comparison with the evaluation of the global significance
provided by the method of trial factors is also provided.