Parallelization of maximum likelihood fits on CPU and GPU: Algorithms and Technologies

by Alfio Lazzaro (CERN)

Aula Milla (INFN - Padova)

Aula Milla

INFN - Padova


Data analyses based on maximum likelihood fits are commonly used for fitting statistical models to data samples. Large data samples and complex likelihood functions models can be very time-consuming tasks. Therefore, it becomes particularly important to speed-up the evaluation of the likelihood functions. In this presentation Alfio will present an algorithm which benefits from data vectorization and parallelization on CPU and GPU. Thereafter I will discuss the implementation technologies for porting the application on both devices.

Organized by

Franco Simonetto