By default, TensorFlow attempts to utilize all available CPU cores to maximize performance. This is achieved through its backend, which typically employs TensorFlow's built-in thread pool for parallel task processing. For instance, when handling extensive matrix operations, TensorFlow automatically distributes these computations across multiple cores to accelerate the overall process.
For example, when training a deep neural network, TensorFlow can send different data batches to various processor cores for processing. This parallel processing significantly reduces training time.
However, it is worth noting that while TensorFlow defaults to leveraging multi-core advantages, users can still customize core usage through configuration options. For instance, you can restrict TensorFlow to use only a portion of the CPU cores or assign specific operations to particular cores.
Additionally, for GPU usage, TensorFlow also attempts to utilize the GPU's multiple compute units to accelerate processing, which similarly reflects its design philosophy of maximizing resource utilization by default.
In summary, TensorFlow defaults to utilizing all available processor cores (whether CPU or GPU) as much as possible, but this can be adjusted according to user requirements.