Speaker
Mr
Gilles GRASSEAU
(Laboratoire Leprince-Ringuet (IN2P3/CNRS))
Description
Multidimensional integration based on Monte-Carlo (MC) techniques are widely
used in High Energy Physics (HEP) and numerous other computing domains. In HEP, they naturally arise from the multidimensional probability densities or from the likelihoods often present in the analysis.
Today HPC programming requires dealing with computing accelerators
like GPGPU or 'many-core' processors, but also taking into account the development
portability and the hardware heterogeneities with the use of open programming standard
like OpenCL.
Among several MC possible algorithms (MISER, Markov Chain, etc.) the choice has
been driven by the popularity and the efficiency of the method. The 'Vegas' algorithm is
frequently used in the LHC analysis as it is accessible from ROOT environment and provides
reasonably good performance.
The parallel implementation of Vegas for computing accelerators presents no major
obstacle, however some technical difficulties occur when dealing with portability
and heterogeneity mainly due to the lack of libraries and development tools (like
performance analysis tools).
Combining MPI and OpenCL, we will present a scalable
distributed implementation. Performance will be shown on different platforms (NVidia K20, Intel Xeon Phi) but
also on heterogeneous platform mixing CPUs, and different kind of computing accelerator
cards.
The presented work is a canvas to integrate various multidimensional
functions for different analysis processes. It is planned to integrate and exploit this
implementation in the future CMS analysis processes.
Primary author
Mr
Gilles GRASSEAU
(Laboratoire Leprince-Ringuet (IN2P3/CNRS))
Co-authors
Mr
David CHAMONT
(Laboratoire Leprince-Ringuet (IN2P3/CNRS))
Mr
Stanislav LISNIAK
(Laboratoire Leprince-Ringuet (IN2P3/CNRS))
