Towards hardware acceleration for parton densities estimation
High Energy Physics - Phenomenology
2019-09-25 v1 Machine Learning
High Energy Physics - Experiment
Abstract
In this proceedings we describe the computational challenges associated to the determination of parton distribution functions (PDFs). We compare the performance of the convolution of the parton distributions with matrix elements using different hardware instructions. We quantify and identify the most promising data-model configurations to increase PDF fitting performance in adapting the current code frameworks to hardware accelerators such as graphics processing units.
Cite
@article{arxiv.1909.10547,
title = {Towards hardware acceleration for parton densities estimation},
author = {Stefano Carrazza and Juan Cruz-Martinez and Jesús Urtasun-Elizari and Emilio Villa},
journal= {arXiv preprint arXiv:1909.10547},
year = {2019}
}
Comments
6 pages, 2 figures, 3 tables, in proceedings of PHOTON 2019