This demonstration released at GTC Digital 2020 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.
The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.
RAPIDS relies on NVIDIA CUDA primitives for low-level compute optimization, and 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.
View the demo:
