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探索HEP的代码可移植性解决方案,使用粒子跟踪测试代码
Hammad Ather1, Sophie Berkman2, Giuseppe Cerati3
1Department of Computer and Information Science, University of Oregon, Eugene, OR, United States.
Frontiers in big data
|November 7, 2024
概括
高能物理 (HEP) 实验必须利用各种计算资源,如GPU. 使用代码可移植性工具实现最佳性能需要仔细实施,因为结果因内存布局和编译器选择等因素而有很大差异.
科学领域:
- 高能物理 高能物理
- 计算科学 计算科学
- 软件工程 软件工程 软件工程
背景情况:
- 传统的高能物理 (HEP) 实验严重依赖于x86 CPU.
- 即将到来的实验如DUNE和高发光率LHC将大幅增加计算需求.
- 利用各种各样的计算资源,包括来自不同供应商的图形处理单元 (GPU),对于满足这些需求至关重要.
研究的目的:
- 评估HP的不同代码可移植性解决方案的性能和实施经验.
- 使用HEP跟踪算法测试案例,将便携式代码实现与参考x86 CPU版本进行比较.
- 识别影响多个架构可移植代码执行性能的因素.
主要方法:
- 来自HEP跟踪算法的测试代码被用于对各种可移植性解决方案进行基准测试.
- 在不同的可移植性工具中进行了性能比较,包括基于编译器实用方法和抽象库.
- 分析了诸如内存布局,内存固定策略和编译器选择等因素对性能的影响.
主要成果:
- 便携式实现在几个实例中表现出接近参考代码的性能.
- 观察到显著的性能差异,高度依赖于具体的实施细节.
- 即使对于相对简单的测试应用程序,也很难实现最佳性能.
结论:
- 代码可移植性工具为HEP计算提供了可行的解决方案,但实现峰值性能需要仔细考虑实施的具体情况.
- 存储器布局,固定策略和编译器选择是影响便携式代码效率的关键因素.
- 编译器和可移植性工具的持续开发对于它们在未来的HEP实验中成功部署至关重要.
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