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We propose a novel generic inverted index framework on the GPU (calledGENIE), aiming to reduce the programming complexity of the GPU for parallelsimilarity search of different data types. Not every data type and similaritymeasure are supported by GENIE, but many popular ones are. We present thesystem design of GENIE, and demonstrate similarity search with GENIE on severaldata types along with a theoretical analysis of search results. A new conceptof locality sensitive hashing (LSH) named $\tau$-ANN search, and a novel datastructure c-PQ on the GPU are also proposed for achieving this purpose.Extensive experiments on different real-life datasets demonstrate theefficiency and effectiveness of our framework. The implemented system has beenreleased as open source.

A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report