Oct 10 – 14, 2016
San Francisco Marriott Marquis
America/Los_Angeles timezone

Performance studies of GooFit on GPUs versus RooFit on CPUs while estimating the statistical significance of a new physical signal

Oct 13, 2016, 3:30 PM
1h 15m
San Francisco Marriott Marquis

San Francisco Marriott Marquis

Poster Track 5: Software Development Posters B / Break


Graphical Processing Units (GPUs) represent one of the most sophisticated
and versatile parallel computing architectures available that are nowadays
entering the High Energy Physics field. GooFit is an open source tool
interfacing ROOT/RooFit to the CUDA platform on nVidia GPUs (it also
supports OpeMP). Specifically it acts as an interface between the MINUIT
minimization algorithm and a parallel processor which allows a Probability
Density Function (PDF) 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 technique has been implemented both in
ROOT/RooFit and GooFit frameworks with the purpose of estimating the local
statistical significance of the structure observed by CMS close to the
kinematical threshold of the J/psi phi invariant mass in the B+ to J/psi
phi K+ decay. The optimized GooFit application running on GPUs provides
striking speed-up performances with respect to the RooFit application
parallelised on multiple CPU workers through the PROOF-Lite tool. The
described technique has been extended to situations when, dealing with an
unexpected signal, a global significance must be estimated.The LEE 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 considerably increases, thus the RooFit-based approach is not only
time-expensive but gets unreliable and the use of GooFit as a reliable
tool is mandatory to carry out this p-value estimation method.

Primary Keyword (Mandatory) Analysis tools and techniques
Secondary Keyword (Optional) Parallelizarion

Primary authors

Adriano Di Florio (Universita e INFN, Bari (IT)) Alexis Pompili (Universita e INFN, Bari (IT))

Presentation materials