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README.rst

@@ -674,6 +674,13 @@ GPU Rendering
   of ``GPU`` massive parallelism for float point calculations required for
   the rendering process.
 
+* While initially designed for rendering graphics, GPUs have found applications in various fields beyond graphics rendering. Technologies like CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) allow developers to leverage GPU power for general-purpose computing (GPGPU). This has led to advancements in fields such as artificial intelligence, cryptography, computational finance, and scientific research, where parallel processing capabilities can be exploited to solve complex problems more efficiently than traditional CPUs.
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+* GPUs excel in handling large datasets and performing computations on massive amounts of data. Their architecture allows for efficient memory bandwidth and high throughput, which is essential for rendering detailed graphics, processing big data, and training large neural networks. This capability makes GPUs a preferred choice for tasks that require the processing of vast amounts of information quickly and accurately.
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+* GPUs are designed with a large number of cores that can execute thousands of threads simultaneously, making them highly efficient for parallel processing tasks. This is particularly useful in rendering, where multiple pixels or vertices can be processed at the same time. The parallel nature of GPUs significantly accelerates complex computations, making them ideal for tasks like real-time graphics rendering, scientific simulations, and machine learning.
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 Window Server
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