How to compile Tensorflow with SSE4.2 and AVX instructions?

Step 1: Verify Hardware and Software Compatibility

First, verify that your processor supports the SSE4.2 and AVX instruction sets. This can be confirmed by checking the CPU's official documentation or using tools such as cpuinfo. Second, ensure that a compiler supporting these instruction sets, such as GCC or Clang, is installed.

Step 2: Install Required Dependencies

Compiling TensorFlow requires multiple dependencies, including but not limited to Bazel (build tool), Python, and numpy. Refer to the official documentation for a complete list of dependencies and installation instructions.

Step 3: Configure TensorFlow Source Code

To obtain the TensorFlow source code, clone the official GitHub repository:

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

Next, run the configuration script and set options as needed:

bash
./configure

During configuration, the system will ask whether to enable optimizations such as SSE4.2 and AVX. Select 'Yes' based on your system's support.

Step 4: Modify Build Configuration

Open the .bazelrc file in the TensorFlow source directory to ensure appropriate compiler optimization flags are enabled. For example:

plaintext
build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl --copt=-march=native
build:mkl --copt=-O3

Here, -march=native instructs the compiler to automatically enable optimization options best suited for the current processor, including SSE4.2 and AVX.

Step 5: Compile TensorFlow

Build your TensorFlow version using Bazel. This may take a considerable amount of time depending on system performance:

bash
bazel build //tensorflow/tools/pip_package:build_pip_package

Step 6: Package and Install

After building, create a Python wheel package and install it:

bash
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-VERSION-tags.whl

Example: Performance Comparison

To verify the improvements from using SSE4.2 and AVX instruction sets, compare TensorFlow's performance on specific tasks (e.g., model training or inference) before and after compilation optimizations. Typically, enabling these instruction sets significantly improves floating-point operation speed, thereby reducing training time or enhancing inference speed.

Conclusion

This is the process for compiling TensorFlow with SSE4.2 and AVX instruction sets. By doing so, you can fully leverage the advanced features of modern processors to optimize TensorFlow's runtime efficiency and performance.