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About this paper
Fei Fang, Yi Liu, Chen Qian
This paper presents d-HNSW, an RDMA-based vector similarity search engine designed for disaggregated memory architectures. It improves graph-based Approximate Nearest Neighbor search by achieving substantial reductions in query latency and increased throughput while managing dynamic data updates.
Key insights
- A co-design approach that integrates HNSW graph structures with RDMA operations is essential to overcome performance bottlenecks like 'pointer-chasing' in disaggregated memory.
- Strategic data partitioning and hierarchical indexing can significantly reduce the amount of data transferred over the network and improve target sub-HNSW identification.
- Pipelining and batched RDMA operations are critical for achieving high throughput and low latency by effectively parallelizing data fetching and computation in disaggregated architectures.
Summary by alphaXiv.
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