Julia 1.10, a programming language that combines high performance, support for dynamic typification, and built-in means for parallel programming, has been released. With Julia’s syntax being similar to Matlab and borrowing elements from Ruby and Lisp, its line manipulation method resembles Perl. The project code is distributed under the MIT license.
Key features of the language:
- High performance: one of the key goals of the project is to achieve performance close to programs in SI. The Julia compiler is based on the developments of the LLVM project and generates effective native machine code for many target platforms.
- Support for various programming paradigms, including elements of object-oriented and functional programming. The standard library also provides functions for asynchronous input/output, process management, logging, profiling, and packet management.
- Dynamic typification: the language does not require a clear definition of types for variables, similar to script programming languages. It also supports an interactive mode of operation.
- Optional possibility of a clear indication of types.
- Syntax excellently suitable for numerical calculations, scientific calculations, machine learning systems, and data visualization. Support for many numerical data types and means for decorating calculations.
- Possibility of a direct call of functions from libraries in the language without additional interlayers.
The main changes in Julia 1.10:
- New language opportunities:
- By default, a new improved syntactic analyzer JuliaSyntax.jl is involved. If you need to return to the old, you should set the environment variable Julia_us_flisp_parser to a value of 1.
- Symbols ⥺ (U+297a, Leftarrowsubset) and ⥷ (U+2977, LeftarRowless) can now be used as binary operators with a priority of arrow.
- Changes in the language:
- With the generation of subsidiaries, the local RNG (
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