Which library is designed for graph analytics and provides a collection of graph algorithms?

The library designed specifically for graph analytics and offering a collection of graph algorithms is cuGraph. This library is part of the RAPIDS AI suite and leverages the parallel processing capabilities of NVIDIA GPUs to efficiently execute complex graph algorithms. cuGraph provides a range of functionalities, including but not limited to, graph traversal, community detection, and centrality measures, making it suitable for use in various applications that require extensive graph data processing.

In contrast, the other options serve different purposes. cuOpt is focused on optimization problems, cuML provides machine learning libraries for GPU, and CUDA is a parallel computing platform and application programming interface (API) for GPU programming, rather than a specific library for graph analytics. Thus, cuGraph stands out for its specialized capabilities in graph processing.