Overview

The use of deep learning libraries like Keras and TensorFlow is widespread in today's data-driven world. These libraries are built to leverage multi-core processors to parallelize computations and improve performance. However, there are scenarios where you might want to limit the number of CPU cores utilized by TensorFlow, such as resource-sharing considerations on a multi-user server, debugging, or running experiments with controlled variables.

In TensorFlow, `intra_op_parallelism_threads` is commonly used to set the number of threads for operations like matrix multiplication. However, there are instances where adjusting this setting doesn't produce the expected constraints on core utilization.

Understanding Thread Pools in TensorFlow

TensorFlow has two major types of thread pools:

1. Intra-op Parallelism: Managed by the `intra_op_parallelism_threads` setting, it governs the number of threads used for the execution of an operation's internal parallelism. For example, matrix operations split into smaller sub-operations executed simultaneously can use this setting.
2. Inter-op Parallelism: Governed by `inter_op_parallelism_threads`, it controls the number of parallel executions of independent operations. Unlike `intra_op`, this setting impacts how many operations can be simultaneously dispatched.

The relationship between these settings can become complex, especially when multiple layers or operations are continuously executed, as is often the case in deep neural networks. Misconfigurations or dependencies can result in `intra_op_parallelism_threads` appearing ineffective.

Why `intra_op_parallelism_threads` Might Not Work as Expected

1. Dynamic Thread Management: TensorFlow employs a dynamic thread management system that may override user settings based on load or operation demands. This dynamic behavior can make it seem as though core limitations set via `intra_op_parallelism_threads` are ignored.
2. OpenMP Interference: The OpenMP library, frequently used for performance optimization in libraries that TensorFlow depends on (such as BLAS for linear algebra), might control the number of threads independently from your Keras/TensorFlow settings.
3. OS-Based Scheduling: The operating system's own processes and settings may interfere with thread management, making it challenging to isolate and control computational resources with TensorFlow settings alone.
4. GPU Coexistence: If a model mixes GPU and CPU operations, it might influence behavior related to thread allocation, as CUDA streams on the GPU can impact scheduling decisions for CPUs.

Examples and Workarounds

Example 1: Setting the Number of CPU Cores (Ineffective Instance)