QuadRank: Engineering a High Throughput Rank

2026-02-04

AI Summary

This work addresses the critical need for efficient multi-threaded rank queries in high-throughput bioinformatics applications, where such operations are pivotal for accelerating pattern matching. The authors propose BiRank and QuadRank—two compact rank data structures that uniquely integrate cache-line inline offsets with mid-block design, extended to the four-character DNA alphabet. By constraining popcount computation ranges and incorporating prefetching mechanisms, the approach ensures only a single cache miss per query. Experimental results demonstrate that QuadRank achieves up to twice the query speed of existing methods, and when combined with prefetching, reaches four times the throughput of Genedex while using less memory. System performance is ultimately bounded by dual-channel DDR4 memory bandwidth.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Machine Learning: Learning Preferences or Rankings

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Given a text, a query $\mathsf{rank}(q, c)$ counts the number of occurrences of character $c$ among the first $q$ characters of the text. Space-efficient methods to answer these rank queries form an important building block in many succinct data structures. For example, the FM-index is a widely used data structure that uses rank queries to locate all occurrences of a pattern in a text. In bioinformatics applications, the goal is usually to process a given input as fast as possible. Thus, data structures should have high throughput when used with many threads. Contributions. For the binary alphabet, we develop BiRank with 3.28% space overhead. It merges the central ideas of two recent papers: (1) we interleave (inline) offsets in each cache line of the underlying bit vector [Laws et al., 2024], reducing cache-misses, and (2) these offsets are to the middle of each block so that only half of them need popcounting [Gottlieb and Reinert, 2025]. In QuadRank (14.4% space overhead), we extend these techniques to the $\sigma=4$ (DNA) alphabet. Both data structures require only a single cache miss per query, making them highly suitable for high-throughput and memory-bound settings. To enable efficient batch-processing, we support prefetching the cache lines required to answer upcoming queries. Results. BiRank and QuadRank are around $1.5\times$ and $2\times$ faster than similar-overhead methods that do not use inlining. Prefetching gives an additional $2\times$ speedup, at which point the dual-channel DDR4 RAM bandwidth becomes a hard limit on the total throughput. With prefetching, both methods outperform all other methods apart from SPIDER [Laws et al., 2024] by $2\times$. When using QuadRank with prefetching in a toy count-only FM-index, QuadFm, this results in a smaller size and up to $4\times$ speedup over Genedex, a state-of-the-art batching FM-index implementation.

Problem

• rank query
• high throughput
• succinct data structures
• FM-index
• DNA alphabet

Innovation

• rank query
• succinct data structure
• cache-efficient
• prefetching
• high-throughput