Performance Analysis of Array Database Systems in Non-Uniform Memory Architecture

Array Database Management Systems (Array databases) support query processing over multi-dimensional data. Data storage is implemented with non-linear structures to mitigate the shortcomings of the relational model when dealing with raw binary data, such as images, time series, and others. Due to data-hungry nature of multi-dimensional data applications, array databases must ideally provide a linear speedup when using a multi-processing system. When dealing with Non-Uniform Memory Access (NUMA) machines, array databases may require massive data movement across the nodes resulting in a severe performance impact, depending on the user operation. In this paper, we analyze the performance impact of the NUMA architecture in the SAVIME and SciDB array databases running five different well-known static thread pinning strategies. Our experiments showed a maximum speedup of these different strategies by 2.49x for SAVIME and up to 1.40x for SciDB. We also observed that these static strategies only yield 48% from the potential speedup (and 26% of the energy reduction), opening a new research topic.