Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools -- including compiler pragma-based approaches, abstraction libraries, and other tools -- allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions.
@article{arxiv.2409.09228,
title = {Exploring code portability solutions for HEP with a particle tracking test code},
author = {Hammad Ather and Sophie Berkman and Giuseppe Cerati and Matti Kortelainen and Ka Hei Martin Kwok and Steven Lantz and Seyong Lee and Boyana Norris and Michael Reid and Allison Reinsvold Hall and Daniel Riley and Alexei Strelchenko and Cong Wang},
journal= {arXiv preprint arXiv:2409.09228},
year = {2024}
}