Which GPU feature is critical for accelerating deep learning workloads?

The ability to execute parallel operations across thousands of cores is critical for accelerating deep learning workloads because deep learning tasks often involve processing large datasets that can be computed simultaneously. GPUs are specifically designed to handle parallel processing effectively, allowing them to perform multiple calculations at once, significantly speeding up the training and inference phases of deep learning models.

Deep learning algorithms, particularly those used in neural networks, require substantial amounts of matrix multiplications and other mathematical operations that benefit immensely from parallel execution. By leveraging the architecture of a GPU, which contains numerous cores dedicated to computation, practitioners can efficiently train models on large-scale data while reducing the time and resources needed.

Other features, while beneficial in certain contexts, do not directly contribute to the acceleration of deep learning workloads in the same way. For instance, while having a large amount of onboard cache memory, lower power consumption, or high clock speeds is useful, the essence of deep learning efficiency lies in the GPU's capability to handle massive parallel processing tasks effectively.