As data volumes and computational complexity of data analysis techniques have increased, so has the need for acceleration of these workloads to allow the data scientist to quickly iterate on models. One of the key ways to achieve this has been through GPU acceleration. Traditionally, GPU acceleration has required specialized knowledge of low-level C++ GPGPU programming. However, the open-source RAPIDS data science libraries allow data scientists to easily make use of GPU acceleration in common ETL, machine learning, and graph analytics workloads using familiar Python APIs (e.g. pandas and scikit-learn).
This workshop will introduce RAPIDS, walk through its component libraries, and will show participants how these libraries allow them to easily introduce GPU acceleration into their workflows to speed up compute times and increase iteration on their models. We will demonstrate common data ETL (cuDF), machine learning (cuML), graph analytics (cuGraph), signal processing (cuSignal), spatial analytics (cuSpatial), and InfoSec (cyber log accelerator) workloads that RAPIDS accelerates. We will also discuss how users can integrate RAPIDS and the broader open-source GPU data science ecosystem to solve their specific use cases.
An understanding of basic data science concepts will be helpful, but is not required. No experience with GPU programming is required!