LiteTopK: Exploiting the Curse of Dimensionality for a Fused Indexer-TopK Kernel in Long-Context Sparse Attention

Abstract

Indexer-TopK, the operation to compute the scores and select the top-k candidates, is widely used by sparse attention algorithms in large language models and vector retrieval in recommendation systems and vector databases. However, existing GPU-based Indexer-TopK kernels like DeepSeek Sparse Attention (DSA) remain inefficient due to excessive global memory traffic, costly synchronization, and prohibitive memory overhead. In this study, inspired by the curse of dimensionality phenomenon, we first observe that sparse attention scores exhibit a score concentration phenomenon, where scores tend to fall within a narrow range. Based on this observation, we propose LITETOPK, an efficient fused Indexer-TopK kernel. LITETOPK first samples a small subset of data to estimate query-data score ranges, then partitions candidates into bins accordingly. This organization allows the LITETOPK kernel to maintain a tight approximate threshold online, write back only promising candidates, reduce unnecessary I/O and memory overhead while preserving exact Top-k correctness. Building on LITETOPK, we further propose LITEDSA, which exploits the similarity of top-k candidate sets among neighboring tokens. LITEDSA packs neighboring tokens' candidates for joint computation and masks out extra scores for each query, thereby reducing memory traffic while preserving correctness. Experimental results in a real-world deployment environ ment with eight B200 GPUs show that LITETOPK+LITEDSA accelerates the prefill stage of GLM 5.2 by 1.35x, with no performance loss and lower memory overhead.