Getting Started

  • In Julia, the REPL is much more important than in some other languages.
  • Pluto is a reactive environment
  • VS Code has the best editor integration for Julia

Introduction to Julia

  • Julia has if-else, for, while, function and module blocks that are not dissimilar from other languages.
  • Blocks are all ended with end.
  • …
  • Julia variables are not visible outside the block in which they’re defined (unlike Python).

Types and Dispatch

  • Julia is fundamentally a dynamically typed language.
  • Static types are only ever used for dispatch.
  • Multiple dispatch is the most important means of abstraction in Julia.
  • Parametric types are important to achieve type stability.

Simulating the Solar System

  • Plots.jl is in some ways the ‘standard’ plotting package, but it is in fact quite horrible.
  • Makie.jl offers a nice interface, pretty plots, is generally stable and very efficient once things have been compiled.

Packages and environments

  • Julia has a built-in package manager called Pkg.
  • The package manager is usually accessed from the REPL, by pressing ].
  • Project.toml lists the dependencies of a package.
  • Manifest.toml specifies a completely reproducible environment with exact versions.

Package development

  • Currently, the editor with (by far) the best support is VSCode.
  • The Revise.jl module can automatically reload parts of your code that have changed.
  • Best practice: file names should reflect module names.

Best practices

  • Julia has integrated support for unit testing.
  • Documenter.jl is the standard package for generating documentation.
  • The Julia ecosystem is well equiped to help you keep your code in fit shape.

Julia and performance

  • The Julia compiler compiles a function once its called with a specific type signature.

Reducing allocations on the Logistic Map

  • Allocations are slow.
  • Growing arrays dynamically induces allocations.

Performance: do's and don'ts

  • Don’t use mutable global variables.
  • Write your code inside functions.
  • Specify element types for containers and structs.
  • Specialize functions instead of doing manual dispatch.
  • Write functions that always return the same type (type stability).
  • Don’t change the type of variables.

Channels and do-syntax

  • Channels are Julia’s primary means of state management and inter-thread communication.

Getting Started

  • Basic parallel for-loops are done with the @threads macro.
  • Julia has built-in support for atomics.
  • Channels are primary means of asynchronous communication.

Getting Started

  • Julia has built-in support for distributed computing.

Transducers

  • Transducers are composable functions.
  • Using the |> operator can make your code consise, with fewer parenthesis, and more readable.
  • Overusing the |> operator can make your code an absolute mess.
  • Using Tranducers you can build scalable data pipe lines.
  • Many Julia libraries for parallel computing and data processing are built on top of Tranducers.

Recommended libraries

  • SciML toolkit is dominant but not always the best choice.
  • Search for libraries that specify your needs.

GPU Programming

  • We can compile Julia code to GPU backends using KernelAbstractions.
  • Even on smaller laptops, significant speedups can be achieved, given the right problem.

Appendix: Iterators and type stability

  • When things are slow, try adding more types.
  • Writing abstract code that is also performant is not easy.

Appendix: Generating Julia Fractals

Appendix: Lorenz attractor