Statistical significance estimation of a signal within the GooFit framework on GPUs
1 Universitá degli Studi di Bari
2 INFN - Sezione di Bari
a e-mail: firstname.lastname@example.org
Published online: 22 March 2017
In order to test the computing capabilities of GPUs with respect to traditional CPU cores a high-statistics toy Monte Carlo technique has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose to estimate the statistical significance of the structure observed by CMS close to the kinematical boundary of the J/ψϕ invariant mass in the three-body decay B+ → J/ψϕK+. GooFit is a data analysis open tool under development that interfaces ROOT/RooFit to CUDA platform on nVidia GPU. The optimized GooFit application running on GPUs hosted by servers in the Bari Tier2 provides striking speed-up performances with respect to the RooFit application parallelised on multiple CPUs by means of PROOF-Lite tool. The considerable resulting speed-up, evident when comparing concurrent GooFit processes allowed by CUDA Multi Process Service and a RooFit/PROOF-Lite process with multiple CPU workers, is presented and discussed in detail. By means of GooFit it has also been possible to explore the behaviour of a likelihood ratio test statistic in different situations in which the Wilks Theorem may or may not apply because its regularity conditions are not satisfied.
© The Authors, published by EDP Sciences, 2017
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