RAPIDS: GPU-Accelerated Data Analytics & Machine Learning

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA CUDA primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

This demonstration uses RAPIDS, and OmniSci’s GPU-accelerated analytics platform to quickly visualize and run queries on the 1.1 billion New York City taxi ride dataset.

"To learn more about RAPIDS and to try using GPU-accelerated analytics using and open-source version of OmniSci (including sample data), please visit