In Julia, we can easily create an integer or a float: Let’s move to the overview of the Julia aspects I mentioned earlier. It works the same way for other types (and type combinations) as well. To make the function fast for two floats we just need to call it with any two floats and the compiler will specialise the compiled function for those types too. So after it’s first called with two integers, every other call with any integers will be way faster (and will take less memory). It’s because Julia’s compiler specialises the function’s compiled code on the basis of its first usage for given argument-types. In this presentation, I plan to focus on the following aspects of Julia:īefore we start, let's see JIT compiler in action:Īs you can see, some calls to add are significantly faster and require much less memory than the others. On top of that, Julia offers powerful metaprogramming capabilities. But once you get the hang of it, you’re bound to see some spectacular results. To make the most of it, developers need to know how to use the typing system properly. It's fast thanks to the JIT (just-in-time) compiler and a smart typing system that helps the compiler to optimize the code. Julia was designed to perform high-performance numerical and scientific computing. It's free (basically like every language save for Matlab or Mathematica).It's easy to learn and quick to prototype with (like Python, and unlike C, C++, Java).It's fast (like C or C++, and unlike Python or Ruby).Why should you care? Julia has a lot of benefits: Let’s see what it can offer to Python developers. Julia is a relatively new programming language.
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