Parallel selection on GPUs

Abstract We present a novel parallel selection algorithm for GPUs capable of handling single rank selection (single selection) and multiple rank selection (multiselection). The algorithm requires no assumptions on the input data distribution, and has a much lower recursion depth compared to many state-of-the-art algorithms. We implement the algorithm for different GPU generations, always leveraging the respectively-available low-level communication features, and assess the performance on server-line hardware. The computational complexity of our SampleSelect algorithm is comparable to specialized algorithms designed for – and exploiting the characteristics of – “pleasant” data distributions. At the same time, as the proposed SampleSelect algorithm does not work on the actual element values but on the element ranks of the elements only, it is robust to the input data and can complete significantly faster for adversarial data distributions. We also address the use case of approximate selection by designing a variant that radically reduces the computational cost while preserving high approximation accuracy.