Accelerating Cross Filtering with cuDF
RAPIDS is all about enabling data scientists with enterprise grade tools and GPU performance. Visualization being a key component in a data scientist’s toolbox, we are naturally working on ways to accelerate that experience.
I’m a huge advocate of data visualization. It’s one of the closest things we have to a universal language that can distill and communicate complex, supported ideas to a broad domain of audiences. And although the types of visualizations out there are as varied as the types of data, the ability to quickly explore concurrent views of multivariate data is universally useful. In short, we wanted cross filtering.
We’ve just started exploring ways to use RAPIDS for visualization, both for data computation and rendering acceleration. CuXfilter is our first experiment. We started this project because we wanted to see how we could use the impressive deck.gl / kepler.gl visualization libraries and are eager to keep working with the Uber viz team. Yet, visualization is a big space, and we are eager to further engage and integrate with the wider open source data visualization ecosystem — specifically those in Python. Whether you’re a developer or end user of a library, we’d love to hear from you on how we can continue to grow!
Want to make your own visualizations with cuXfilter? Have some ideas on how to better the architecture or apply GPU acceleration? We want to hear it, so raise some issues or PRs on the cuXfilter github. Curious about other ways you can contribute, find more at rapids.ai.