Improved Computation of Database Operators via Vector Processing Near-Data

Data-centric applications are increasingly more common, causing issues brought on by the discrepancy between processor and memory technologies to be increasingly more apparent. Near-Data Processing (NDP) is an approach to mitigate this issue. It proposes moving some of the computation close to the memory, thus allowing for reduced data movement and aiding data-intensive workloads. Analytical database queries are very commonly used in NDP research due to their intrinsics usage of very large volumes of data. In this paper, we investigate the migration of most time-consuming database operators to 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 Vector-In-Memory Architecture (VIMA) to a highperformance x86 baseline. We pitch VIMA against both a single-thread baseline and a modern 16-thread x86 system to evaluate its performance. Against a single-thread baseline, our experiments show that VIMA is able to speed up execution by up to 5x for selection, 2.5x for projection, and 16x for join while consuming up to 99% less energy. When considering a multi-thread baseline, VIMA matches the execution time performance even at the largest dataset sizes considered. In comparison to existing state-of-the-art NDP platforms, we find that our approach achieves superior performance for these operators.