Online Multimedia Similarity Search with Response Time-Aware Parallelism and Task Granularity Auto-Tuning

Abstract

This paper presents an efficient parallel implementation of the Product Quantization based approximate nearest neighbor multimedia similarity search indexing (PQANNS). The parallel PQANNS efficiently answers nearest neighbor queries by exploiting the ability of the quantization approach to reduce the data dimensionality (and memory demand) and by leveraging parallelism to speed up the search capabilities of the application. Our solution is also optimized to minimize query response times under scenarios with fluctuating query rates (load) as observed in online services. To achieve this goal, we have developed strategies to dynamically select the parallelism configuration and task granularity that minimizes the query response times during the execution. The proposed strategies (ADAPT and ADAPT+G) were thoroughly evaluated and have shown, for instance, to reduce the query response times in 6.4x as compared to the best static configuration of parallelism and task granularity.