浮点一致性交叉验证方法：用于可重复和互操作的DDA求解器的公平基准测试
Floating-point consistent cross-verification methodology for reproducible and interoperable DDA solvers with fair benchmarking
AI总结：
本文提出了一种统一的软件辅助方法，用于验证和基准测试三个主要开源DDA求解器，通过统一自由参数实现机器精度一致，并提供性能比较和配置指南。
摘要：
离散偶极近似（DDA）是一种广泛使用的数值方法，用于求解任意形状物体的电磁散射。尽管其流行，但独立实现之间的定量比较仍具挑战性，因为存在线性系统约定、求解器设置和默认数值参数的差异。本文介绍了一种统一的软件辅助方法，用于验证和基准测试三个主要开源DDA求解器：DDSCAT、ADDA和IFDDA。我们展示了如何通过统一所有自由参数在不同实现之间实现机器精度一致，并提供实用等价表，以实现可重复和互操作的模拟。使用该方法，我们进行了系统性的CPU和GPU性能比较，涵盖OpenMP、MPI和CUDA/OpenCL并行化。除了基准测试外，我们的方法还为配置一致的DDA模拟和理解精度、求解器选择和硬件架构如何影响运行时间、可扩展性和准确性提供了实用指南。软件包还支持回归测试和位级可重复性验证，以支持未来代码版本的测试。
英文摘要：
The discrete dipole approximation (DDA) is a widely used and versatile numerical method for solving electromagnetic scattering by arbitrarily shaped objects. Despite its popularity, quantitative comparisons between independent implementations remain challenging due to differences in linear-system conventions, solver settings, and default numerical parameters. In this work, we introduce a unified software-assisted methodology for cross-verification and benchmarking of three major open-source DDA solvers: DDSCAT, ADDA, and IFDDA. We demonstrate how machine-precision agreement can be achieved across implementations by aligning all free parameters and provide practical equivalence tables enabling reproducible and interoperable simulations. Using this methodology, we perform systematic CPU and GPU performance comparisons covering OpenMP, MPI, and CUDA/OpenCL parallelization. Beyond benchmarking, our approach serves as a practical guide for configuring consistent DDA simulations and for understanding how precision, solver choice, and hardware architecture affect runtime, scalability, and accuracy in computational light-scattering studies. The software package also supports regression testing and bitwise reproducibility verification for future code releases.
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