GPU Acceleration
GPU acceleration refers to the use of a graphics processing unit (GPU) to perform computations in parallel, which can significantly speed up certain types of data processing tasks. GPUs are designed to handle large amounts of data simultaneously, making them ideal for tasks such as machine learning, deep learning, and other types of artificial intelligence applications. By offloading some of the processing work from the CPU to the GPU, data scientists and researchers can achieve faster results and more efficient use of computing resources. GPU acceleration is particularly useful for tasks that involve large datasets, complex algorithms, and deep neural networks. Many popular machine learning frameworks, such as TensorFlow and PyTorch, have built-in support for GPU acceleration, making it easy for developers to take advantage of this technology.
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