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Julia for Data Science by Zacharias Voulgaris PhD

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APPENDIX E: Parallelization in Julia

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When we talk about parallelization in Julia, we refer to the use of different CPUs (or GPUs) either within your computer or a computer cluster. This can be done using the base package and with just a few lines of code, making it a powerful tool. Parallelization is particularly useful in stochastic processes (e.g. Monte-Carlo simulations), linear algebra calculations, and any other processes that can be broken down into independent components. Even optimization has a lot to benefit from this technique, particularly if you are going deep into AI territory.

Parallelization is extremely important in data science ...

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