Abstract

The field of similarity search on metric spaces has been widely studied in the last years, mainly because it has proven suitable for a number of application domains such as multimedia retrieval and computational biology, just to name a few. To achieve efficient query execution throughput, it is essential to exploit the intrinsic parallelism in respective search algorithms. Many strategies have been proposed in the literature to parallelize these algorithms either on shared or distributed memory multiprocessor systems. More recently, GPUs have been proposed to evaluate similarity queries for small indexes that fit completely in GPU’s memory. However, most of the real databases in production are much larger. In this paper, we propose multi-GPU metric space techniques that are capable to perform similarity search in datasets large enough not to fit in memory of GPUs. Specifically, we implemented a hybrid algorithm which makes use of CPU-cores and GPUs in a pipeline. We also present a hierarchical multi-level index named List of Superclusters (LSC), with suitable properties for memory transfer in a GPU.