利用粒子径迹测试代码探索 HEP 的代码可移植性解决方案
Exploring code portability solutions for HEP with a particle tracking test code
AI总结：
本文以 HEP 径迹算法测试代码为对象，比较基于编译器 pragma、抽象库等代码可移植性方案在多厂商 GPU 等架构上的性能与实现体验。
摘要：
传统上，高能物理（HEP）实验的大部分重要计算需求都依赖 x86 CPU。随着该领域展望 DUNE 和高亮度 LHC 等下一代实验，计算需求预计将大幅增长。为应对这一增长，有必要利用所有可用的计算资源，包括不同厂商的 GPU。多种多样的代码可移植性工具——包括基于编译器 pragma 的方法、抽象库以及其他工具——使同一源代码能够在多种架构上高效运行。本文中，我们使用取自一个 HEP 径迹算法的测试代码，比较不同可移植性解决方案的性能和实现体验。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
