Search Results for « HNSW »

The rapid growth of Large Language Models (LLMs) has generated massive amounts of high-dimensional feature vectors extracted from diverse datasets. Efficient storage and retrieval of such data are critical for enabling accurate and fast query responses. Vector databases (Vector DBs) provide efficient storage and retrieval for high-dimensional vectors. These systems rely on Approximate Nearest Neighbor Search (ANNS) indexes, such as HNSW, to handle large-scale data efficiently. However, the original HNSW is implemented on DRAM, which is both costly and vulnerable to crashes. Therefore, we propose P-HNSW, a crash-consistent HNSW on persistent memory. To guarantee crash consistency, P-HNSW introduces two logs, NLog and NlistLog. We describe the logging process during the operation and the recovery process in the event of system crashes. Our experimental results demonstrate that the overhead of the proposed logging mechanism is negligible, while P-HNSW achieves superior performance compared with SSD-based recovery mechanisms.

Hierarchical Navigable Small World (HNSW) has demonstrated impressive accuracy and low latency for high dimensional nearest neighbor searches. However, its high computational demands and irregular, large-volume data access patterns present significant challenges to search efficiency. To address these challenges, we introduce P-HNSW, a software-hardware codesign solution for HNSW that integrates a Principal Component Analysis Filter (PCAF). On the software side, we apply PCAF to reduce dataset dimensionality, thereby lowering the volume of neighbor access and decreasing the computational load for distance calculations. On the hardware size, we design the P-HNSW processor with custom instructions to optimize search throughput and energy efficiency. Experimental results from RTL design synthesized using a 65nm technology node show that the proposed P-HNSW implementation achieves an 11.65× increase in Queries per Second (QPS) and a 56.8% reduction in energy consumption, compared to the original HNSW implementation

Approximate Nearest Neighbor Search (ANNS) algorithms use various techniques to perform an approximate similarity search on vector databases, exchanging the optimality of exhaustive search for drastic time savings. The HNSW algorithm for ANNS uses a hierarchical Proximity Graph (PG) to reduce the number of hops during search. However, HNSW cannot produce diverse results because it uses a single entry point. In this work, we propose iHNSW, a novel cluster-based approach to enable iterative search of the HNSW graph. Empirical experiments on SIFT1M, GIST, MNIST, and Fashion-MNIST vector datasets show that our approach consistently leads to an increase in recall metrics at the cost of extending search time.

Hierarchical Navigable Small World (HNSW) is widely adopted for approximate nearest neighbor search (ANNS) for its ability to deliver high recall with low latency on large-scale, high-dimensional embeddings. The exploration factor, commonly referred to as ef, is a key parameter in HNSW-based vector search that balances accuracy and efficiency. However, existing systems typically rely on manually and statically configured ef values that are uniformly applied across all queries. This results in a distribution-agnostic configuration that fails to account for the non-uniform and skewed nature of real-world embedding data and query workloads. As a consequence, HNSW-based systems suffer from two key practical issues: (i) the absence of recall guarantees, and (ii) inefficient ANNS performance due to over- or under-searching. In this paper, we propose Adaptive-ef (Ada-ef), a data-driven, update-friendly, query-adaptive approach that dynamically configures ef for each query at runtime to approximately meet a declarative target recall with minimal computation. The core of our approach is a theoretically grounded statistical model that captures the similarity distribution between each query and the database vectors. Based on this foundation, we design a query scoring mechanism that distinguishes between queries requiring only small ef and those that need larger ef to meet a target recall, and accordingly assigns an appropriate ef to each query. Experimental results on real-world embeddings produced by state-of-the-art Transformer models from OpenAI and Cohere show that, compared with state-of-the-art learning-based adaptive approaches, our method achieves the target recall while avoiding both over- and under-searching, reducing online query latency by up to 4x, offline computation time by 50x, and offline memory usage by 100×.

Approximate nearest neighbor (ANN) search is a fundamental problem in computer science for which in-memory graph-based methods, such as Hierarchical Navigable Small World (HNSW), perform exceptionally well. To scale beyond billions of high-dimensional vectors, the index must be distributed. The disaggregated memory architecture physically separates compute and memory into two distinct hardware units and has become popular in modern data centers. Both units are connected via RDMA networks that allow compute nodes to directly access remote memory and perform all the computations, posing unique challenges for disaggregated indexes. In this work, we propose a scalable HNSW index for ANN search in disaggregated memory. In contrast to existing distributed approaches, which partition the graph at the cost of accuracy, our method builds a graph-preserving index that reaches the same accuracy as a single-machine HNSW. Continuously fetching high-dimensional vector data from remote memory leads to severe network bandwidth limitations, which we overcome by employing an efficient caching mechanism. Since answering a single query involves processing numerous unique graph nodes, caching alone is not sufficient to achieve high scalability. We logically combine the caches of the compute nodes to increase the overall cache effectiveness and confirm the efficiency and scalability of our method in our evaluation.

Hierarchical Navigable Small World (HNSW) is widely adopted for approximate nearest neighbor search (ANNS) for its ability to deliver high recall with low latency on large-scale, high-dimensional embeddings. The exploration factor, commonly referred to as ef, is a key parameter in HNSW-based vector search that balances accuracy and efficiency. However, existing systems typically rely on manually and statically configured ef values that are uniformly applied across all queries. This results in a distribution-agnostic configuration that fails to account for the non-uniform and skewed nature of real-world embedding data and query workloads. As a consequence, HNSW-based systems suffer from two key practical issues: (i) the absence of recall guarantees, and (ii) inefficient ANNS performance due to over- or under-searching. In this paper, we propose Adaptive-ef (Ada-ef), a data-driven, update-friendly, query-adaptive approach that dynamically configures ef for each query at runtime to approximately meet a declarative target recall with minimal computation. The core of our approach is a theoretically grounded statistical model that captures the similarity distribution between each query and the database vectors. Based on this foundation, we design a query scoring mechanism that distinguishes between queries requiring only small ef and those that need larger ef to meet a target recall, and accordingly assigns an appropriate ef to each query. Experimental results on real-world embeddings produced by state-of-the-art Transformer models from OpenAI and Cohere show that, compared with state-of-the-art learning-based adaptive approaches, our method achieves the target recall while avoiding both over- and under-searching, reducing online query latency by up to 4x, offline computation time by 50x, and offline memory usage by 100x.

Efficient vector query processing is critical to enable AI applications at scale. Recent solutions struggle with growing vector datasets that exceed single-machine memory capacity, forcing unnecessary data movement and resource underutilization in monolithic architectures. We present d-HNSW, the first disaggregated vector similarity search engine for RDMA-based remote memory systems that achieves high performance while supporting fast data indexing with low network communication overhead. The core of d-HNSW is a novel disaggregation of the graph-based vector indexing data structure HNSW. It exploits the characteristics of greedy searching in HNSW to efficiently coordinate data transfers from the memory pool to the compute pool while serving data requests. Specifically, it leverages three ideas: (i) Representative index caching, a lightweight index constructed from a sampled subset of data, is cached in the compute pool to reduce frequent access to critical components of the hierarchical graph-based index, (ii) RDMA-friendly data layout design to reduce the networking round trips incurred by vector query and insertion and (iii) batched query-aware data loading to reduce bandwidth usage on data transfer between pools, addressing the limited cache capacity in compute nodes. We evaluate d-HNSW with extensive benchmarking datasets. The experimental results show that d-HNSW outperforms Naive d-HNSW implementation by up to 117x in latency while maintaining recall as 0.87 in dataset SIFT1M@1.