SING: Sequence Indexing Using GPUs

Abstract

Data series similarity search is a core operation for several data series analysis applications across many domains. This has attracted lots of interest that led to the development of several indexing techniques. Nevertheless, these techniques fail to deliver the similarity search time performance that is needed for interactive exploration, or analysis of large data series collections. We propose SING, the first data series index designed to take advantage of Graphics Processing Units (GPUs). SING is an in-memory index that uses CPU+GPU co-processing (as well as SIMD, multi-core and multi-socket architectures), in order to accelerate similarity search. Our experimental evaluation with synthetic and real datasets shows that SING is up to 5.1x faster than the state-of-the-art parallel in-memory approach, and up to 62x faster than the state-of-the-art parallel serial scan algorithm. SING achieves exact similarity search query times as low as 32msec on 100GB datasets, which enables interactive data exploration on very large data series collections.