Which terminology describes the architecture that accelerates NVIDIA calculations?

The architecture that accelerates NVIDIA calculations is known as CUDA. CUDA, which stands for Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows developers to use a programming language, such as C, C++, and Fortran, to write algorithms that can be executed on NVIDIA GPUs (Graphics Processing Units). This enables significantly faster processing for computational tasks by leveraging the massive parallel processing capabilities of the GPU.

In contrast, attention mechanisms are techniques used primarily in machine learning, especially in natural language processing, to improve the performance of models by allowing them to focus on different parts of the input data. ROUGE is a metric used for evaluating automatic summarization and translation, specifically by comparing the overlap of n-grams between a model-generated output and reference outputs. cuGraph is a library for graph analytics in CUDA but is more specialized than CUDA in general. Thus, CUDA is the foundational technology that enables the accelerated computations in NVIDIA architectures.