Advanced SmartNICs known as Data Processing Units (DPU) enable in-network data analytics, being equipped with standard processors and accelerators capable of doing custom computation on-NIC. These SmartNICs are advantageous because the host CPU can delegate simpler data analytics tasks, like filtering, to the NIC where the data first arrives. Only data needing further refinement must be passed on to the host CPU. Our benchmarks focus on NVIDIA’s commercially available Bluefield-3 DPU.

Similarity searches are a critical task in data mining. As datasets grow larger, exact nearest neighbor searches quickly become unfeasible, leading to the adoption of approximate nearest neighbor (ANN) searches. ANN has been studied for text data, images, and trajectories. However, there has been little effort to develop ANN systems for polygons in spatial database systems and geographic information systems. We present PolyMinHash, a system for approximate polygon similarity search that adapts MinHashing into a novel 2D polygon-hashing scheme to generate short, similarity-preserving signatures of input polygons. Minhash is generated by counting the number of randomly sampled points needed before the sampled point lands within the polygon's interior area, yielding hash values that preserve area-based Jaccard similarity.