用内存计算加速数据聚合可视化，让模式一目了然。
A Data Aggregation Visualization System supported by Processing-in-Memory
- 通过频次归一化分箱，让高频数据占更多像素
- 在1亿行32列数据上1分钟完成近5000个聚合图生成
- 采用内存计算比传统CPU快45%-64%，适合大数据探索
数据聚合的可视化是数据探索与数据分析中最常见的手段之一，有助于发现数据中的相关性与模式。我们提出DIVAN系统，通过按频率自动归一化一维轴，生成大量二维可视化图像。DIVAN利用分箱对输入数据进行归一化，使出现频率更高的数据值占据更多像素。该系统可基于CPU或存算一体（PIM）架构快速计算聚合结果以支持可视化。在真实数据集上，我们展示了DIVAN生成的可视化能揭示预期和意外的模式。使用PIM技术，我们在大规模数据集上的聚合计算速度比现代CPU提升45%至64%。对于包含1亿行、32列的数据集，系统可在约一分钟内完成4,960个聚合图像（每个尺寸为128x128x128）的计算。
原文摘要 · Abstract (English)
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.
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