python run cuda kernel

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Numba is a Just-In-Time (JIT) compiler for Python that translates functions annotated with Numba decorators into optimized machine code. It has support for CUDA, allowing you to run parallel computations on NVIDIA GPUs using CUDA kernels. In this tutorial, we'll explore how to write and execute CUDA kernels in Python using Numba.
Before starting the tutorial, make sure you have the following installed:
You can install Numba using the following command:
Let's start by creating a simple CUDA kernel that adds two arrays element-wise. Save the following code in a file named cuda_add.py.
In this code, we define a CUDA kernel named add_kernel, which adds two arrays element-wise. The main function generates random input arrays, transfers them to the GPU, launches the CUDA kernel, and copies the result back to the host.
To run the CUDA kernel, execute the following command in your terminal:
Ensure that your GPU is properly configured and that the required NVIDIA CUDA toolkit is installed. If everything is set up correctly, you should see the input arrays (a and b) along with the resulting array printed to the console.
This example demonstrates a basic CUDA kernel in Python using Numba. You can extend and modify this code to perform more complex parallel computations on GPU-accelerated hardware.
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