Why do GPUs have a significant advantage over CPUs in deep learning tasks?

GPUs have a significant advantage over CPUs in deep learning tasks primarily because they are designed to handle parallel processing efficiently. Deep learning often involves performing the same operation on large datasets, such as matrix multiplications and convolutions, which can be computationally intensive.

GPUs excel at this type of workload because they consist of thousands of smaller cores that can carry out many operations simultaneously. This architecture allows them to process multiple data streams or tasks concurrently, significantly speeding up the training and inference processes involved in deep learning. As a result, models that would take a considerable amount of time to train on a CPU can be trained much faster on a GPU.

The other options do not accurately depict the primary advantage that GPUs have in this context. While cooling and power consumption are important factors in performance, they are not the distinctive features that define the advantages of GPUs for deep learning. Similarly, higher clock speed, while beneficial for certain tasks, is not as impactful as the architecture designed for parallel processing in the context of deep learning workloads.