Range Query Processing in a Multi-GPU Environment

Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.