What is the most compelling reason to choose GPUs over CPUs for processing large datasets and complex matrix operations?

The most compelling reason to choose GPUs over CPUs for processing large datasets and complex matrix operations lies in the GPUs' exceptional capability for parallel processing. GPUs are designed with a large number of cores that can perform simultaneous operations on multiple data points. This architecture is particularly suited for tasks that involve extensive calculations, such as those found in AI and machine learning, where large datasets must be processed concurrently.

In the context of complex matrix operations, which are fundamental to various algorithms in AI, the ability to handle many operations in parallel significantly speeds up the computation. This is especially important for deep learning applications, where neural networks require extensive matrix multiplications and additions. The parallel processing capability of GPUs allows them to outperform traditional CPUs, which are optimized for sequential processing and have a limited number of cores available for handling multiple tasks simultaneously.

The other options, while they present valid characteristics of GPUs and CPUs, do not capture the primary advantage for the specific context of processing large datasets and matrix operations as compellingly as their parallel processing capabilities do. For instance, while power efficiency and memory sizes are important factors, they are not the direct reasons for choosing GPUs in high-performance computing scenarios like AI training and inference. Higher single-thread performance, although beneficial for certain tasks, does not hold as