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Title: Introduction to CUDA in Python with Code Examples
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface model developed by NVIDIA. It allows developers to use NVIDIA GPUs for general-purpose processing (GPGPU). In this tutorial, we'll explore how to use CUDA in Python using the pycuda library.
CUDA allows you to offload parallelizable computations to the GPU, which can significantly accelerate certain types of algorithms. It consists of two main parts: the host (CPU) and the device (GPU). The host launches kernels (small programs) on the device, which then performs the computation in parallel.
To begin, install the pycuda library using the following command:
Let's start with a simple example that adds two vectors on the GPU.
This example demonstrates the basic structure of a CUDA Python program. The add_vectors kernel is executed on the GPU, and the result is copied back to the CPU.
CUDA enables you to harness the power of NVIDIA GPUs for parallel processing in Python. This tutorial covers a simple vector addition example, but CUDA can be used for a wide range of parallel computations. Explore the official CUDA documentation and pycuda documentation for more advanced features and optimizations. Happy GPU programming!
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