Enhanced-precision global sums are key to reproducibility in exascale applications. We examine two classic summation algorithms and show that vectorized versions are fast, good and reproducible at exascale. Both 256-bit and 512-bit implementations speed up the operation by almost a factor of four over the serial version. They thus demonstrate improved performance on global summations while retaining the numerical reproducibility of these methods.

中文翻译：

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快速、良好且可重复：求和、矢量化和再现性

提高精度的全局总和是百亿亿级应用中重现性的关键。我们检查了两种经典的求和算法，并表明矢量化版本在百亿亿级上快速、良好且可重现。与串行版本相比，256 位和 512 位实现的操作速度提高了近四倍。因此，它们在保持这些方法的数值再现性的同时，证明了全局求和的性能得到了提高。