Web Reference: Sep 3, 2019 · And that's essentially Julia's secret sauce: since it's always specializing its types on each function, if those functions themselves can infer the output, then the entire function can be inferred and generate optimal code, which is then optimized by the compiler and out comes an efficient function. In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. Now these lectures and notes serve as... In the following sections, we briefly go through a few techniques that can help make your Julia code run as fast as possible. Any code that is performance critical should be inside a function. Code inside functions tends to run much faster than top level code, due to how Julia's compiler works.
YouTube Excerpt: In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course.
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