# RadiK: Scalable and Optimized GPU-Parallel Radix Top-K Selection

arXiv: https://arxiv.org/abs/2501.14336 · HTML: https://mlsanity.com/paper/2501.14336/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2025.

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

- [Guess-Verify-Refine: Data-Aware Top-K for Sparse-Attention Decoding on Blackwell via Temporal Correlation](https://mlsanity.com/paper/2604.22312/) (2026)
- [Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models](https://mlsanity.com/paper/2604.08297/) (2026)
- [A Faster Generalized Two-Stage Approximate Top-K](https://mlsanity.com/paper/2506.04165/) (2025)
- [SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification](https://mlsanity.com/paper/2607.20475/) (2026)
- [Prof-K: Probabilistic One-Pass Filtering for Efficient Top-k Selection](https://mlsanity.com/paper/2608.12573/) (2026)
- [Exact Quantile Balancing and Load-Error Injection for Mixture-of-Experts](https://mlsanity.com/paper/2609.28053/) (2026)

## Abstract (arXiv)

Top-k selection, which identifies the largest or smallest k elements from a data set, is a fundamental operation in data-intensive domains such as databases and deep learning, so its scalability and efficiency are critical for these high-performance systems. However, previous studies on its efficient GPU implementation are mostly merge-based and rely heavily on the fast but size-limited on-chip memory, thereby limiting the scalability with a restricted upper bound on k. This work introduces a scalable and optimized GPU-parallel radix top-k selection that supports significantly larger k values than existing methods without compromising efficiency, regardless of input length and batch size. Our method incorporates a novel optimization framework tailored for high memory bandwidth and resource utilization, achieving up to 2.5x speedup over the prior art for non-batch queries and up to 4.8x speedup for batch queries. In addition, we propose an adaptive scaling technique that strengthens the robustness, which further provides up to 2.7x speedup on highly adversarial input distributions.