Advancing Database System Operators with Near-Data Processing

As applications become more data-intensive, issues like von Neumann’s bottleneck and the memory wall became more apparent since data movement is the main source of inefficiency in computer systems. Looking to mitigate this issue, Near-Data Processing (NDP) moves computation from the processor to the memory, thus reducing the data movement required by many data-intensive workloads. In this paper, we look to database query operators, common targets of NDP research as database systems often need to deal with large amounts of data. We investigate the migration of most time-consuming database operators to Vector-In-Memory Architecture (VIMA), a novel 3D-stacked memory-based NDP architecture. We consider the selection, projection, and bloom join database query operators, commonly used by data analytics applications, comparing VIMA to a high-performance x86 baseline. Our results show speedups of up to 8× for selection, 6× for projection, and 16× for join while consuming up to 99% less energy. To the best of our knowledge, these results outperform the state-of-the-art for these operators on NDP platforms.