Towards hardware acceleration for parton densities estimation
Stefano Carrazza, Juan Cruz-Martinez, Jes\'us Urtasun-Elizari, Emilio, Villa
TL;DR
This paper explores how hardware accelerators like GPUs can improve the computational efficiency of estimating parton distribution functions, addressing current performance bottlenecks in PDF fitting processes.
Contribution
It evaluates the performance of different hardware instructions for PDF convolution and identifies promising configurations for GPU acceleration.
Findings
GPU instructions can significantly speed up PDF convolution tasks
Certain data-model configurations are more suitable for hardware acceleration
Adapting existing code frameworks to GPUs enhances PDF fitting performance
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.
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Quantum Chromodynamics and Particle Interactions · Computational Physics and Python Applications
MethodsConvolution