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Julia is a high-level, high-performance dynamic programming language for technical computing. It addresses the two-language problem by combining the ease of use of languages such as R and Python with the performance of C and Fortran.

Julia is a high-level, high-performance dynamic programming language for technical computing. It addresses the two-language problem by combining the ease of use of languages such as R and Python with the performance of C and Fortran. Julia provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Environments such as Juno and Jupyter provide a rich development environment with interactive graphics.

Community contributed libraries continue to be added at a rapid pace. Programs written in Julia are organized around multiple dispatch - by defining and overloading functions with different combinations of argument types. Part of what makes Julia so expressive is that argument types are user-defined.

The Julia compiler includes a parser written in Scheme ( FemtoLisp), some compiler passes and the runtime in C, code generation through LLVM using C++, and other compiler passes (type inference, inlining, etc.) as well as much of the Base library in Julia itself. For just-in-time generation of 64-bit or 32-bit optimized machine code the LLVM compiler framework is used.

Julia has foreign function interfaces for C, C++, Python, R, and Java, to name a few. Julia can also be embedded in any other software through its C API. Many of these interfaces are high performance and avoid copying data to the extent possible.

Resources for Julia:

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Publications