RAPIDS is an open-source suite of software libraries and APIs designed to accelerate data science workflows on GPUs. It allows data scientists to perform data manipulation and analysis tasks that are traditionally done using CPU-based libraries like Pandas and Scikit-Learn, but with significantly improved performance. The key components of RAPIDS include cuDF, cuML, and cuGraph, which provide functionalities for data manipulation, machine learning, and graph analytics, respectively.

One of the main advantages of RAPIDS is its ability to handle large datasets efficiently. For instance, it can process millions of records much faster than traditional CPU-based methods. The API of cuDF is modeled after Pandas, making it easier for users to transition to RAPIDS without needing to learn a new syntax. This compatibility allows for seamless integration into existing data science workflows.

Additionally, RAPIDS leverages GPU acceleration to enhance performance in various tasks, such as t-SNE visualization and feature engineering, providing substantial speedups compared to CPU implementations ⁴⁵.