NVQuery: Efficient Query Processing in Nonvolatile Memory

Today's computing systems use a huge amount of energy and time to process basic queries in database. A large part of it is spent in data movement between the memory and processing cores, owing to the limited cache capacity and memory bandwidth of traditional computers. In this paper, we propose a nonvolatile memory-based query accelerator, called NVQuery, which performs several basic query functions in memory including aggregation, prediction, bit-wise operations, join operations, as well as exact and nearest distance search queries. NVQuery is implemented on a content addressable memory and exploits the analog characteristic of nonvolatile memory in order to enable in-memory processing. To implement nearest distance search in memory, we introduce a novel bit-line driving scheme to give weights to the indices of the hits during the search operation. To further improve the energy efficiency, our design supports configurable approximation by adaptively putting memory blocks under voltage overscaling. Our experimental evaluation shows that a NVQuery can provide 49.3 x performance speedup and 32.9 x energy savings as compared to running the same query on traditional processor. Approximation improves the energy-delay product (EDP) of NVQuery by 7.3 x, while providing acceptable accuracy. In addition, NVQuery can achieve 30.1 x EDP improvement as compared to the state-of-the-art query accelerators.