Скачать или смотреть Efficiently Return Eigen::Matrix Arrays from C+ + to Python Using Pybind11

A comprehensive guide on how to return Eigen::Matrix arrays from C+ + to Python without unnecessary copying using Pybind11, ensuring better performance with numpy arrays.
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Efficiently Return Eigen::Matrix Arrays from C+ + to Python Using Pybind11

When working with C+ + and Python in scientific computing, sometimes you will encounter challenges related to performance, especially when transferring data between these two languages. One common scenario arises from generating arrays of Eigen matrices in C+ + and needing to use them in Python while avoiding unnecessary data copies. In this post, we will explore a solution to efficiently return Eigen::Matrix arrays from C+ + to Python using Pybind11.

The Problem

You have a C+ + code base that utilizes the powerful Eigen library for matrix computations. Your objective is to generate arrays of matrices in C+ + and return those to Python in a format that can easily be used with NumPy without incurring performance penalties due to data copying.

The specific requirements include:

Generating two nested lists or NumPy arrays: mat_a and mat_b.

Performing calculations for multiple inputs in a single function call.

Ensuring the returned structures do not involve unnecessary matrix copies, which can degrade performance.

The Solution

To address these needs, we will leverage Pybind11 features to expose our C+ + functionality to Python. The basic workflow will involve creating the matrices in Python, then passing a reference of these matrices to C+ + for manipulation.

Step 1: Adjust the C+ + Code

In your C+ + code, instead of returning nested vectors or arrays directly, you will accept a NumPy array from Python and fill it in place:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Create the Python Wrapper

We need a way to call your C+ + function from Python. This is done by creating a simple Python script that utilizes your C+ + extension:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Build the Extension

To compile the C+ + code, you can use a setup.py script:

[[See Video to Reveal this Text or Code Snippet]]

You can then build the extension by running:

[[See Video to Reveal this Text or Code Snippet]]

A Note on Performance

When utilizing numpy and providing a pointer buffer of the matrix in C+ + , you ensure that the data remains in contiguous memory, which is crucial for performance. This method avoids copies and allows efficient operations on the data structure. Instead of using std::vector<std::array<...>>, consider using Eigen::Array, which allows better memory management and access patterns.

Conclusion

In this guide, we discussed how to efficiently return nested Eigen::Matrix arrays from C+ + to Python using Pybind11, preventing unnecessary copying of data and improving performance. By following the steps outlined above, you will be able to create seamless integrations between your C+ + computations and Python applications, taking full advantage of both ecosystems.

Feel free to extend the provided example further by integrating more complex matrix operations and inputs, ensuring your code is performant and maintainable.