Can GPU Graph Algorithms Boost Vector Search Performance by 10×?

Why Use GPU Graph Algorithms?

In the data‑driven era, fast and accurate retrieval of massive unstructured data is essential for cutting‑edge AI applications. Traditional CPU‑based solutions struggle with the memory bandwidth and throughput required for large‑scale vector search.

OpenSearch Vector Search GPU Edition

OpenSearch has launched a GPU‑enabled vector search service on Alibaba Cloud, the first in China to support GPU specifications for vector retrieval. By exploiting GPU parallelism, the solution delivers nearly tenfold performance gains, making it ideal for high‑QPS scenarios.

What Is Vector Search?

Unstructured data such as images, audio, and dialogue are transformed into multi‑dimensional vectors (embedding). Similarity is measured by vector distance, and the top‑similar results are retrieved. Applications include image search, price comparison, personalized search, and semantic understanding.

Approximate Nearest Neighbor (ANNS) Overview

ANNS efficiently finds near‑neighbors in large datasets, trading a small loss in accuracy for significant speed gains. Common ANNS algorithms include:

Tree‑based methods : KD‑Tree, Annoy – simple but less effective in high dimensions.

Hash‑based methods : LSH – maps similar items to the same hash bucket.

Quantization methods : SQ and PQ – reduce storage and computation by quantizing vectors.

Clustering methods : Hierarchical Clustering – uses cluster centers for fast pruning.

Graph‑based methods : HNSW – builds multi‑layer small‑world graphs for fast search.

GPU Graph Algorithm Advantages

The GPU graph algorithm leverages massive parallelism to construct and search neighbor graphs, offering high throughput and efficient index building. Supported GPU specs include various NVIDIA T4 configurations (e.g., 4‑core 15 GB, 8‑core 31 GB, 16‑core 62 GB, 24‑core 93 GB).

Performance Test: Throughput

Comparing GPU (T4, V100, A800) with CPU‑based HNSW on the ANN_GIST1M dataset (1 M × 960‑dim vectors) shows:

Recall ≥ 95%: up to 53× speedup (batch = 32, A800).

Recall ≥ 99%: up to 45× speedup.

Recall ≥ 99.5%: up to 46× speedup.

Single‑Node Cluster Throughput

Using a T4 GPU in OpenSearch, the system achieves:

Recall 95% batch = 32: QPS = 15 712 (≈ 9.7× CPU).

Recall 99% batch = 32: QPS = 8 080 (≈ 9.36× CPU).

Recall 99.5% batch = 32: QPS = 5 500 (≈ 9.27× CPU).

Performance Test: Index Building

GPU‑accelerated index construction dramatically reduces time and size:

CPU: 1 103 s, index size 3.8 GB.

T4 GPU: 85 s, index size 2.2 GB (≈ 58 % of CPU size).

V100 GPU: 44 s.

A800 GPU: 19 s.

Conclusion

By adopting OpenSearch's GPU‑enabled vector search and graph algorithms, enterprises and developers can achieve up to tenfold improvements in search performance, making it highly suitable for high‑QPS AI workloads while reducing costs.