Dr. Top-k: Delegate-Centric Top-k on GPUs
This work addresses a performance bottleneck in GPU-based top-k queries, which is incremental but offers strong specific gains for data processing and database systems.
The paper tackles the problem of inefficient top-k computation on GPUs by introducing Dr. Top-k, a delegate-centric system that reduces workloads by over 99% and consistently outperforms state-of-the-art methods.
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 $β$ 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.