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 fast but size-limited on-chip memory, thereby limiting scalability with a restricted upper bound on k. This work introduces RadiK, 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. RadiK incorporates a novel optimization framework tailored for high memory bandwidth and resource utilization, achieving up to 2.5 × speedup over the prior art for non-batch queries and up to 4.8 × speedup for batch queries. In addition, we propose an adaptive scaling technique that strengthens robustness, which further provides up to 2.7 × speedup on highly adversarial input distributions.

By identifying the k largest or smallest elements in a set of data, top-k selection is critical for modern high-performance databases and machine learning systems, especially with large data volumes. However, previous studies on its GPU implementation are mostly merge-based and rely heavily on the high-speed but size-limited on-chip memory, thereby resulting in a restricted upper bound on k. This paper introduces RadiK, a highly optimized GPU-parallel radix top-k selection that is scalable with k, input length, and batch size. With a carefully designed optimization framework targeting high memory bandwidth and resource utilization, RadiK supports far larger k than the prior art, achieving up to 2.5× speedup for non-batch queries and up to 4.8× speedup for batch queries. We also propose a lightweight refinement that strengthens the robustness of RadiK against skewed distributions by adaptively scaling the input elements.