论文标题
在GPU上快速，功能丰富的弱压缩SPH：编码策略和编译器选择
Fast, feature-rich weakly-compressible SPH on GPU: coding strategies and compiler choices
论文作者
论文摘要
GPUSPH是使用CUDA完全在GPU上运行的弱压缩平滑粒子流体动力学方法的第一个实现。版本5于2018年6月发布，具有对代码的根本重组，为多个功能提供了更结构化的实现，并对大多数重型计算内核进行了专门优化。尽管这些改进导致了可衡量的性能提升（根据测试案例和硬件配置的不同，从15 \％到30 \％），但它也发现了官方CUDA编译器（\ texttt {nvcc}）的某些限制，尤其是NVIDIA提供的，尤其是NVIDIA提供的限制。这导致努力支持替代编译器，尤其是Clang，并具有令人惊讶的性能提升。
GPUSPH was the first implementation of the weakly-compressible Smoothed Particle Hydrodynamics method to run entirely on GPU using CUDA. Version 5, released in June 2018, features a radical restructuring of the code, offering a more structured implementation of several features and specialized optimization of most heavy-duty computational kernels. While these improvements have led to a measurable performance boost (ranging from 15\% to 30\% depending on the test case and hardware configuration), it has also uncovered some of the limitations of the official CUDA compiler (\texttt{nvcc}) offered by NVIDIA, especially in regard to developer friendliness. This has led to an effort to support alternative compilers, particularly Clang, with surprising performance gains.
