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Julia: Fast as Fortran, easy as Python
Tech-Speak
The Python programming language is wildly popular because it's easy to write, easy to read, and can be made to do almost anything with its huge stack of libraries. Yet, one complaint is heard over and over: It's too slow! This drawback might not matter much for web programming, where your program can spend more time waiting for responses from the network and database than actually computing anything, but data scientists, physicists, and engineers who try to use Python for real number crunching quickly hit the performance wall. A new language called Julia promises to be as easy to program as Python and other dynamic, interpreted languages, while offering the execution speed of statically typed, compiled languages such as C and Fortran.
Dynamic Languages
The popularity of interpreted, dynamically typed languages is easy to understand. You can get right to the real work in your program without spending multiple lines of code on ceremony and bookkeeping. You don't need to declare data types, manage memory, or think about how your high-level code will be translated into machine instructions. Perhaps best of all, you can type expressions into an interactive prompt, often called a REPL (read-eval-print loop), and get immediate results. The REPL allows you to experiment freely, try out ideas, and use your favorite programming language as a sophisticated calculator.
These types of dynamic languages used to be called "scripting languages," because they were used in shell scripting; however, as their applications have grown far beyond their original niche in automating system maintenance tasks, this term is falling out of use. These days Python, Ruby, R, Perl, and other languages in this class are grouped together under various and sometimes inaccurate terms. In this article, I adopt the description "dynamic languages" to reflect the dynamic nature of their data typing. (See
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