A Data Aggregation Visualization System supported by Processing-in-Memory

Abstract: Data visualization of aggregation queries is one of the most common ways of doing data exploration and data science as it can help identify correlations and patterns in the data. We propose DIVAN, a system that automatically normalizes the one-dimensional axes by frequency to generate large numbers of two-dimensional visualizations. DIVAN normalizes the input data via binning to allocate more pixels to data values that appear more frequently in the dataset. DIVAN can utilize either CPUs or Processing-in-Memory (PIM) architectures to quickly calculate aggregates to support the visualizations. On real world datasets, we show that DIVAN generates visualizations that highlight patterns and correlations, some expected and some unexpected. By using PIM, we can calculate aggregates 45%-64% faster than modern CPUs on large datasets. For use cases with 100 million rows and 32 columns, our system is able to compute 4,960 aggregates (each of size 128x128x128) in about a minute.