Performance studies of GooFit while estimating the global statistical significance of a new physical signal

Abstract

Graphical Processing Units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are currently entering the High Energy Physics field. GooFit is an open source tool interfacing ROOT/RooFit to the CUDA platform on nVidia GPUs that acts as an interface between the MINUIT minimization algorithm and a parallel processor which allows a Probability Density Function to be evaluated in parallel.In order to test the computing capabilities of GPUs with respect to traditional CPU cores, a high-statistics pseudo-experiment method has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose of estimating the local statistical significance of an already known signal. The optimized GooFit application running on GPUs provides striking speed-up performances with respect to the RooFit application parallelized on multiple CPU workers by means of the PROOF-Lite tool.This method is extended to situations when, dealing with an unexpected signal, a global significance must be estimated. The Look-Elsewhere-Effect is taken into account by means of a scanning technique in order to consider - within the same background-only fluctuation and everywhere in the relevant mass spectrum - any fluctuating peaking behavior with respect to the background model. The execution time of the fitting procedure for each MC toy can considerably increase, thus the RooFit-based approach gets so time-expensive that may become unreliable while GooFit is an excellent tool to carry reliably out this p-value estimation method.