Sara Mahdizadeh Shahri

arXiv2026

Analyzing and Reducing Search Quality Differences in Vector Similarity Search

Modern database services scalably search over large data collections via Approximate Nearest Neighbor Search, which improves search performance at the cost of search quality, measured by recall. In practice, a database operator seeks to achieve a target mean recall while maximizing throughput across search queries. We show that optimizing for mean recall masks significant differences in recall across queries even when target recall is met. As a result, numerous queries face (1) below-target recall, hurting user experience and revenue and (2) above-target recall, wasting computation to deliver unnecessarily high search quality. Thus, it is critical to detect and reduce recall differences across queries. We design RCheck, a light-weight run-time system that identifies low-recall queries and reduces recall differences while achieving high throughput. RCheck's key design principle is to dynamically, efficiently adapt search effort by increasing effort for queries below target recall and decreasing effort for those above it. RCheck tunes available search effort parameters, making it readily deployable. We evaluate RCheck using the widely-used production-style pgvector database. At the same throughput, RCheck improves mean recall by 11-93% and enables 8-47% more queries to meet target recall compared to the state-of-the-art globally-tuned configuration.

cs.DB↗

arXiv2018

Making Belady-Inspired Replacement Policies More Effective Using Expected Hit Count

Memory-intensive workloads operate on massive amounts of data that cannot be captured by last-level caches (LLCs) of modern processors. Consequently, processors encounter frequent off-chip misses, and hence, lose a significant performance potential. One way to reduce the number of off-chip misses is through using a well-behaved replacement policy in the LLC. Existing processors employ a variation of least recently used (LRU) policy to determine a victim for replacement. Unfortunately, there is a large gap between what LRU offers and that of Belady's MIN, which is the optimal replacement policy. Belady's MIN requires selecting a victim with the longest reuse distance, and hence, is unfeasible due to the need to know the future. Consequently, Belady-inspired replacement polices use Belady's MIN to derive an indicator to help them choose a victim for replacement. In this work, we show that the indicator that is used in the state-of-the-art Belady-inspired replacement policy is not decisive in picking a victim in a considerable number of cases, and hence, the policy has to rely on a standard metric (e.g., recency or frequency) to pick a victim, which is inefficient. We observe that there exist strong correlations among the hit counts of cache blocks in the same region of memory when Belady's MIN is the replacement policy. Taking advantage of this observation, we propose an expected-hit-count indicator for the memory regions and use it to improve the victim selection mechanism of Belady-inspired replacement policies when the main indicator is not decisive. Our proposal offers a 5.2\% performance improvement over the baseline LRU and outperforms Hawkeye, which is the state-of-the-art replacement policy.

cs.AR↗