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arXiv · 2109.08219

Dr. Top-k: Delegate-Centric Top-k on GPUs

Anil Gaihre·Da Zheng·Scott Weitze·Lingda Li·Shuaiwen Leon Song·Caiwen Ding·Xiaoye S Li·Hang Liu
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Abstract

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.

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Anil Gaihre, Da Zheng, Scott Weitze, Lingda Li, Shuaiwen Leon Song, Caiwen Ding, Xiaoye S Li, Hang Liu. 2021-09-16. Dr. Top-k: Delegate-Centric Top-k on GPUs. https://doi.org/10.1145/3458817.3476141

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