This study observes that sparse attention scores exhibit a score concentration phenomenon, where scores tend to fall within a narrow range, and proposes LITETOPK, an efficient fused Indexer-TopK kernel, which exploits the similarity of top-k candidate sets among neighboring tokens and proposes LITEDSA, which exploits the similarity of top-k candidate sets among neighboring tokens.
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.
CoSA is proposed, a two-stage training-free Sparse Attention under proxy-kernel CO-design, which couples a Kernel-Aware Proxy (KAP) with an Ordered-Skipping Kernel (OSK) and achieves a 4.93% attention speedup and reduces end-to-end Time-to-First-Token by 2.53% with negligible performance degradation.
Yufei Xue, Lin Niu, Hong Liu et al.· arXiv.org· 0 citations
Top-$K$ sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries. However, identifying this subset still requires scoring the current query against the full KV cache and performing global Top-$K$ selection, leaving selector cost linear in context length and limiting the practical efficiency of sparse attention for long-context decoding. In this paper, we introduce ReTopK, a training-free method that accelerates dynamic Top-$K$ attention by reusing historical retrieval decisions. ReTopK builds on the observation that similar queries often attend to overlapping supports and that partially overlapping supports can still preserve most of the Exact Top-$K$ attention mass. For each attention head, it maintains a bounded cache of historical query--support pairs, retrieves the most similar cached queries for each new query, unions their stored supports with a recent window, and reranks only the resulting compact candidate set using exact current-query scores. A similarity-based fallback invokes full-history Exact Top-$K$ when reuse is unreliable, while periodic exact refreshes limit cache drift. ReTopK retains the complete KV cache and reuses only selected indices, rather than historical scores, attention weights, or outputs. Across 16K--128K contexts, ReTopK achieves the lowest PG19 perplexity and the highest NIAH and LongBench scores among the evaluated approximate methods. At 128K with $K=512$, ReTopK incurs only a 0.50\% perplexity increase over Exact Top-$K$ while accelerating attention computation by $3.07\times$.
Token-level sparse attention, as implemented by DeepSeek Sparse Attention (DSA) in production systems, makes the downstream attention efficient but shifts the bottleneck to the indexer that feeds it. To select the top-k tokens for each query, the indexer must still score every preceding token, incurring a cost of O(L^2) per layer for a sequence of length L. We observe that this per-query scan is largely redundant: nearby queries select highly overlapping top-k tokens, and the indexer scores are long-tailed along the key axis. We exploit these properties in PIVOT, Proxy Indexing Via One full-prefix Traversal, a training-free, drop-in replacement for the DSA indexer that shares one prefix scan across a group of nearby queries. PIVOT aggregates a group into a single proxy query, performs one shared full-prefix scan to obtain a candidate set, and then selects a top-k for each query from that set. Two variants trade speed for fidelity: PIVOT-Reuse shares the proxy top-k across the group for maximum speed, whereas PIVOT-Refine re-scores the candidate set with the indexer of each query and then selects an individual top-k, matching the dense indexer at a small additional cost. A single algorithm covers both inference phases, differing only in how groups are formed: fixed-size groups of consecutive queries in prefill, and the queries decoded together in one multi-token prediction (MTP) step in decode. On DeepSeek-V3.2 and GLM-5.1 across LongBench and RULER, PIVOT matches the accuracy of the dense DSA indexer while accelerating it by up to 4x and reducing end-to-end latency by up to 1.6x at long context.
Hong Liu, Yuan Cheng, Lin Niu et al.· arXiv.org· 1 citation· ⚡1
Cardinality estimation is at the core of any commercial database system for efficient query processing. Over the decades, non-learning-based estimation techniques (e.g., histogram-based, sampling-based) have been widely used in both commercial and open-source database platforms. However, these techniques are only effective when the number of columns in a table is small, as they cannot properly capture dependencies between multiple attributes. Recently, learning-based approaches have been shown to perform significantly better than the heuristic methods that have been used for the past three decades. Despite this success, existing learned models often struggle to balance memory efficiency and accuracy when dealing with datasets that mix high and low cardinality attributes. In this paper, we propose a deep learning model formally called QEmbed. Our model is built upon the Masked Autoencoder for Distribution Estimation (MADE) auto-regressive framework to learn joint data distributions for selectivity estimation. To improve data representation and overcome the limitations of using a single encoding method, we design a hybrid encoding scheme that combines one-hot and embedding encodings. This hybrid design enables QEmbed to retain fine-grained attribute information for smaller domains while capturing compact semantic patterns for large, sparse domains. We capture attribute correlations by factoring the joint data distribution into a series of conditional probabilities. This approach naturally accommodates both point and range queries. Through extensive experiments, we show that while QEmbed faces a latency trade-off on extremely wide schemas, it provides highly reliable cardinality estimates overall. A key advantage of our model is that it reduces extreme tail errors (maximum Q-errors), avoiding catastrophic estimation failures on complex, highly correlated workloads.
Diffusion Transformers (DiTs) incur quadratic self-attention cost over spatiotemporal tokens. Existing training-free sparse attention methods often construct sparse masks from block-level or cluster-level proxy scores, which can obscure fine-grained differences among keys and miss high contribution keys under aggressive sparsity. Moreover, such proxy scores may yield overly concentrated softmax distributions, causing Top-$p$ to retain too few keys for some query clusters. Although a fixed Top-$k$ minimum alleviates this failure mode, a shared value cannot adapt to variations across heads and inputs. To address both limitations, we propose SCOPE, a training-free sparse attention framework that combines 3D-RoPE-aligned key subspace clustering with online per-head Top-$k$ estimation for efficient video-DiT inference. SCOPE partitions post-RoPE keys into temporal, height, and width subspaces, clusters them independently, and aggregates the corresponding centroid scores through lookup tables to obtain per key proxy scores for each query cluster. Building on existing hybrid Top-$p$/fixed Top-$k$ selection, SCOPE derives a head-specific Top-$k$ value online by averaging the initial retained key counts within each head, weighted by query cluster size, and selects additional keys only for query clusters whose initial retained key counts fall below this value. Sparse attention is then computed over the selected original keys and values. Across six model--task configurations, SCOPE consistently outperforms existing training-free baselines in both fidelity and latency, achieving up to a $1.99\times$ end-to-end speedup on 720p HunyuanVideo with $28.46$ dB PSNR relative to dense attention.
Qi Zhao, Qi-Rui Li, Hanlin Tang et al.· 0 citations
Edge LLM inference combines sparsity and low-bit quantization to meet device memory, latency, and power limits. Yet quantization shrinks weight payloads without proportionally reducing sparse metadata, so index traffic and nonzero extraction become critical SpMM bottlenecks. We introduce the Payload-to-Metadata Ratio (PMR) and show that improving PMR raises effective compute intensity in decoding. We present UnionSparse, an index-efficient framework that combines Index-Efficient Bitmap Encoding (IE-BME) with a SpMM kernel using Low-Bit Shared-Memory Parallel Decoding (LSPD). IE-BME amortizes metadata and aligns sparse traversal with fragment assembly, while LSPD improves small-batch execution. Under W4A4 quantization and 30%--70% sparsity, UnionSparse outperforms FlashLLM and SpInfer by 2.30x and 1.43x, and CUTLASS and cuBLAS Tensor Core by 1.56x and 3.46x, respectively. These results establish payload-extraction efficiency as a first-order concern for low-bit sparse inference on edge GPUs. Source code is available at: https://github.com/Victor-Alen/UnionSparse.
Tianhao Jiang, Hang Gu, Teng Wang et al.· 0 citations
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