Abstract The approach of vertically partitioning the index has long been considered as impractical for building a distributed search engine due to its high communication cost. With the recent surge of interest in using High Performance Computing networks such as Infiniband in the data center, we argue that vertical partitioning is not only practical but also highly scalable. To demonstrate our point, we built a distributed image search engine (VertiCut) that performs multi-round approximate neighbor searches to find similar images in a large image collection
