Efficient Top-K Query Processing on Massively Parallel Hardware

Abstract

A common operation in many data analytics workloads is to find the top-k items, i.e., the largest or smallest operations according to some sort order (implemented via LIMIT or ORDER BY expressions in SQL). A naive implementation of top-k is to sort all of the items and then return the first k, but this does much more work than needed. Although efficient implementations for top-k have been explored on traditional multi-core processors, there has been no prior systematic study of top-k implementations on GPUs, despite open requests for such implementations in GPU-based frameworks like TensorFlow and ArrayFire. In this work, we present several top-k algorithms for GPUs, including a new algorithm based on bitonic sort called bitonic top-k. The bitonic top-k algorithm is up to a factor of \new15x faster than sort and 4x faster than a variety of other possible implementations for values of k up to 256. We also develop a cost model to predict the performance of several of our algorithms, and show that it accurately predicts actual performance on modern GPUs.