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

arXiv: https://arxiv.org/abs/2109.08219 · HTML: https://mlsanity.com/paper/2109.08219/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2021.

## Built on by (most cited first, 6 of 6)

- [Tango: rethinking quantization for graph neural network training on GPUs](https://mlsanity.com/paper/2308.00890/) (2023)
- [LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention](https://mlsanity.com/paper/2607.11976/) (2026)
- [DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification](https://mlsanity.com/paper/2307.03500/) (2023)
- [MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training](https://mlsanity.com/paper/2310.00967/) (2023)
- [A Faster Generalized Two-Stage Approximate Top-K](https://mlsanity.com/paper/2506.04165/) (2025)
- [Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection](https://mlsanity.com/paper/2608.12573/) (2026)

## Abstract (arXiv)

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.