Which library is designed to speed up machine learning algorithms on GPUs?

The chosen answer is correct because cuML is specifically designed to provide high-performance implementations of machine learning algorithms optimized for NVIDIA GPUs. It is part of the RAPIDS AI suite, which aims to accelerate data science and analytics workflows by leveraging GPU resources. cuML offers a range of algorithms that are compatible with existing scikit-learn workflows, making it easier for data scientists to utilize the power of GPUs without having to completely restructure their code or processes.

For additional context, cuGraph is focused on graph analytics, providing tools for working with graph data structures and algorithms. cuDF serves as a GPU DataFrame library, allowing for data manipulation similar to Pandas but optimized for CUDA-enabled GPUs, and is primarily used for data loading and preprocessing. On the other hand, CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA to leverage the power of GPUs for general-purpose processing but is not itself a library specific to machine learning algorithms.