Julia programming language is here to offer a number of features that includes built-in package manager, multiple levels of dispatch, extensive support for both parallel and distributed Julia computing. Being a perfect blend of C, Ruby, Python, R, and MATLAB, it has attained a popular state in the market. Owing to all these features, this technology has got the status of core elements among various business models belonging to companies like NASA, Disney, and Tencent.
People who are practicing Julia latest version are choosing it owing to its performance, speed, superiority, and ease of use. This goes well especially for parallel and distributed Julia computing with the inclusion of TPUs and GPUs. With hundreds of AI and ML projects available in Julia, it is time for us to envision a number of mind-blowing Julia latest version projects in 2020.
Julia development is nothing but a compiled language. Owing to this, it performs faster in comparison to any interpreted language. We cannot categorize Julia development as strictly statically typed. It leverages JIT (Just in Time) compilation for inferring each code variable. It can achieve the speed of C and Go.
You can run your code in parallel using Python while leveraging every CPU core available on the system. You can also import modules with it.
Even Though you have a small number of Julia libraries, you can easily run Python libraries on Julia. You can use C/Fortran libraries and run it directly on Julia.
Similar to Python, Julia is a dynamically-typed language. You can specify types for a variable easily with Julia.
Julia interpreter has the ability to check the argument type whenever you call the reverse. You can easily dispatch the version it matches to the type.
Arrays in Julia are 1-indexed. This makes Julia more intuitive and user-friendly for users with zero-indexed languages.
Julia consists of numerous applications. A few of them are here:
Web programming – both at client and server end,
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