What architectural feature of GPUs makes them more suitable than CPUs for accelerating large-scale deep learning training jobs?

The architectural feature that makes GPUs more suitable than CPUs for accelerating large-scale deep learning training jobs is the massive parallelism provided by thousands of cores. This design allows GPUs to perform many calculations simultaneously, which is crucial for tasks that involve the processing of large datasets, such as those encountered in deep learning.

Deep learning algorithms often require matrix computations and other operations that can be highly parallelized. In contrast to CPUs, which typically have a smaller number of high-performance cores optimized for sequential processing, GPUs are built to handle multiple tasks at once efficiently. The architecture of a GPU allows it to execute thousands of threads concurrently, making it ideal for the high levels of parallelism needed in training complex neural networks.

In comparison, while low power consumption is important in overall system design and could be a consideration in specific use cases, it does not directly impact the efficacy of GPUs for deep learning tasks. Large cache memory, while beneficial for CPU operation, is not a primary factor in the performance of GPUs in deep learning tasks, which focus more on extensive calculations rather than frequent data retrieval from cache. Finally, high core clock speed is a characteristic of some CPUs that enhance single-threaded performance, but it does not compensate for the lack of parallel execution capabilities that GPUs excel in