NVIDIA RAPIDS Brings GPU Acceleration to Data Analytics

The RAPIDS suite of software libraries 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 serialisation costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

XENON NVIDIA Rapids end to end performance chart

Features

Hassle-Free Integration
Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.
Top Model Accuracy
Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
Reduced Training Time
Drastically improve your productivity with near-interactive data science.
Open Source
Customisable, extensible, interoperable – the open-source software is supported by NVIDIA and built on Apache Arrow.

For more information on RAPIDS please contact XENON, visit our RAPIDS Workshop page or https://rapids.ai/.