Optimizing TensorFlow Performance on CPUs with AVX/AVX2 Support

Introduction

TensorFlow is a widely used open-source machine learning library that allows developers to create and train models efficiently. While most applications leverage GPUs for performance, many scenarios still require or exclusively use CPU processing. Modern CPUs come equipped with various instruction sets like AVX (Advanced Vector Extensions) and AVX2, which can significantly boost the computational capabilities of TensorFlow operations. However, not all TensorFlow binaries are optimized to utilize these extensions.

This tutorial will guide you through understanding CPU instructions such as AVX/AVX2, why they matter for TensorFlow, and how to optimize TensorFlow installations on CPUs that support them.

Understanding CPU Instructions: AVX and AVX2

What are AVX and AVX2?

AVX (Advanced Vector Extensions): Introduced by Intel in 2008 with the Sandy Bridge architecture, AVX enhances performance by allowing more data to be processed per clock cycle. It introduces new instructions for handling large vectors of floating-point numbers.
AVX2: An extension of AVX introduced with the Haswell architecture in 2013. AVX2 includes integer operations and additional functionalities that further improve vector processing capabilities, particularly beneficial for linear algebra operations crucial to machine learning tasks.

Why are They Important?

Using these instruction sets can greatly enhance the speed of linear algebra operations—dot products, matrix multiplications, and convolutions—which form the backbone of many machine learning algorithms. By leveraging AVX/AVX2 instructions, TensorFlow can execute these operations more efficiently on supported CPUs, resulting in faster computation times.

Why Might You See a Warning About AVX/AVX2?

When you run TensorFlow, it may output a warning like:

Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

This message indicates that while your CPU is capable of using these advanced instruction sets for improved performance, the current TensorFlow binary installed does not utilize them. This often happens with pre-compiled binaries intended for broad compatibility across different systems.

Optimizing TensorFlow on CPUs

Option 1: Use Pre-Compiled Binaries

For Windows users, consider installing optimized versions of TensorFlow:

Intel MKL Optimization: These are TensorFlow wheels compiled to take advantage of Intel’s Math Kernel Library (MKL) and can significantly improve performance.
```
conda install tensorflow-mkl
```

This package is specifically optimized for Intel CPUs and includes AVX2 support, providing noticeable speedups in inference tasks.

Custom-Wheels from Third Parties: Websites like fo40225’s TensorFlow Windows Wheel offer pre-compiled binaries with specific CPU instruction optimizations. These can be installed using pip:
```
pip install --ignore-installed --upgrade "URL_TO_WHEEL"
```

Option 2: Compile TensorFlow from Source

If you require the most tailored optimization for your system, compiling TensorFlow from source is an option. This process involves setting up a build environment with Bazel (TensorFlow’s build tool) and configuring it to enable AVX/AVX2 support.

Steps to Compile:

Install Dependencies: Make sure all necessary tools and libraries are installed on your system.

Clone TensorFlow Repository:

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow

Configure Build Options: Use the ./configure script to specify AVX/AVX2 support.

Build with Bazel:

bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package

Package and Install:

After a successful build, package TensorFlow into a pip-compatible format and install it.

./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-version-cpXX-cpXXm-linux_x86_64.whl

Option 3: Adjust TensorFlow Settings for GPU Use

If you have access to a GPU, you can still optimize CPU performance by configuring TensorFlow to offload certain operations to the GPU. However, some operations remain on the CPU and can benefit from optimization.

Disable AVX/AVX2 Warnings: You can suppress warnings without taking advantage of these extensions:
```
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
```

This setting prevents warning logs but does not enhance performance.

Conclusion

Optimizing TensorFlow for CPUs with AVX/AVX2 support can lead to significant performance improvements, especially when GPU resources are unavailable or insufficient. By selecting the right installation method—whether pre-compiled binaries or compiling from source—you can ensure your TensorFlow setup is optimized for your specific hardware capabilities. This leads to faster training and inference times, maximizing the efficiency of your machine learning workflows.