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

TL;DR
Dr. Top-k introduces a delegate-centric GPU system that significantly reduces top-$k$ workloads, employs theoretical analysis for optimal subrange sizing, and enables efficient multi-GPU top-$k$ computation, outperforming existing methods.
Contribution
The paper presents a novel delegate-centric design, theoretical analysis for subrange sizing, and four system optimizations for multi-GPU top-$k$ processing.
Findings
Reduces top-$k$ workload by over 99%
Outperforms state-of-the-art methods
Enables fast multi-GPU top-$k$ computation
Abstract
Recent top- computation efforts explore the possibility of revising various sorting algorithms to answer top- queries on GPUs. These endeavors, unfortunately, perform significantly more work than needed. This paper introduces Dr. Top-k, a Delegate-centric top- system on GPUs that can reduce the top- 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 delegate mechanisms to help reduce the workload for top- 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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Caching and Content Delivery · Complexity and Algorithms in Graphs
