  Recent top-$k$ computation efforts explore the possibility of revising
various sorting algorithms to answer top-$k$ queries on GPUs. These endeavors,
unfortunately, perform significantly more work than needed. This paper
introduces Dr. Top-k, a Delegate-centric top-$k$ system on GPUs that can reduce
the top-$k$ workloads significantly. Particularly, it contains three major
contributions: First, we introduce a comprehensive design of the
delegate-centric concept, including maximum delegate, delegate-based filtering,
and $\beta$ delegate mechanisms to help reduce the workload for top-$k$ up to
more than 99%. Second, due to the difficulty and importance of deriving a
proper subrange size, we perform a rigorous theoretical analysis, coupled with
thorough experimental validations to identify the desirable subrange size.
Third, we introduce four key system optimizations to enable fast multi-GPU
top-$k$ computation. Taken together, this work constantly outperforms the
state-of-the-art.
