Introduction to GPUs for Data Analytics

Chapter 6. Interactive Location-Based Intelligence

Just as most organizations now have a need to process at least some data in real time, they also have a growing desire to somehow integrate location into data analytics applications.

As more data becomes available from mobile sources like vehicles and smartphones, there are more opportunities to benefit from analyzing and visualizing the geospatial aspects of this data. But traditional geospatial mapping tools, which were designed primarily for creating static maps, are hardly up to the task.

Analyzing large datasets with any sort of interactivity requires overcoming two fundamental challenges: the lack of sufficient computational power in even today’s most powerful CPUs to handle large-scale geospatial analytics in anything near real time; and the inability of browsers to render the resulting points, lines and polygons in all but the simplest visualizations.

Given its roots in graphics processing, it should come as no surprise that the GPU is especially well-suited to processing geospatial algorithms on large datasets in real time, and rendering the results in map-based graphics that display almost instantly on ordinary browsers (see Figure 6-1). The GPU-accelerated database also makes it possible to ingest, analyze, and render results on a single platform, thereby eliminating the need to move data among different layers or technologies to get the desired results.

Figure 6-1. The GPU-accelerated database is ideally suited for ...