GPU computing
When we use regular CPU computing packages for machine learning, such as Scikit-learn, the amount of parallelization is surprisingly limited because, by default, an algorithm utilizes only one core even when there are multiple cores available. In the chapter about Classification and Regression Trees (CART), we will see some advanced examples of speeding up Scikit-learn algorithms.
Unlike CPU, GPU units are designed to work in parallel from the ground up. Imagine projecting an image on a screen through a graphical card; it will come as no surprise that the GPU unit has to be able to process and project a lot of information (motion, color, and spatiality) at the same time. CPUs on the other hand are designed for sequential processing suitable for tasks where more control is needed, such as branching and checking. In contrast to the CPU, GPUs are composed of lots of cores that can handle thousands of tasks simultaneously. The GPU can outperform a CPU 100-fold at a lower cost...
