Introduction

Learning Objectives

• Understand the core design principles of Julia

• Recognize the main features that make Julia suitable for numerical and scientific computing

• Explain the advantages of multiple dispatch in Julia

• Utilize Julias dynamic typing and memory management capabilities

• Appreciate the high performance capabilities of Julia due to its JIT compilation

Julia

The core goal of Julia was to address the numerical and scientific computing limitations presnet within other programming languages. The overarching motivation for developing the langauge was to combine the ease of use of Python, the dynamism of Ruby and the mathemaatical prowess of MATLAB, with the speed of C.

Core Design Principles

The desing choices within Julia are motivated by a desire to be high-level and high-performance. A key part of the language that will be discussed in depth later is its use of multiple dispatch as a core programming paradigm. Multiple dispatch makes it easier to express many object-oriented and functional programming patterns. Julia is able to choose which version of a function to execute based on the types of all arguments pased to it, enabling optimized code execution for different types.

Further Julia is also designed to be dynamically types, possesses an automatic memory management system, and aims for minimalistic syntax and semantics wihtout reducing functionality.

Main Features

Julia provides support for regular expression, shell-like capabilities and a package managers, simplifing the process of managing and installing packages. Using the LLVM framework Julia is able to achieve high performance from just-in-time (JIT) compilation, allowing for Julia to match performance in reagrding to both C and Fortran for many tasks.