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This is a list of resources and articles about the Julia language.

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14/7/2024 15:47 The main thing in Julia 1.11: Bogumił Kamiński describes a new feature: the @main macro, which defines an entry point.
11/7/2024 19:34 Rankings of Julia books on Amazon: Graphs of Amazon’s sales ranks of their books about the Julia programming language.
8/7/2024 10:46 Julia use in trading leads to massive speedup: Satvik writes on the Julia Discourse: “We use Julia for almost all numeric/data-oriented stuff now, and have moved a lot of the Python code to Julia…At this point 99% of new code I write is Julia, and for the company as a whole it’s about 50-50. The performance differential has become even more ridiculous, an experiment that used to cost ~$125 to run now costs ~.33 cents, and most of those improvements wouldn’t really have been possible in Python.”
1/7/2024 0:19 IdSet in Julia 1.11: A new public type in Julia 1.11 and what it’s good for. As a bonus, a useful summary of Julia’s various equality tests.
18/6/2024 14:12 Julia use in the development of audio codecs for WhatsApp, Instagram and Messenger.: Video in which engineers from Meta describe how they use Julia to develop advanced audio codecs for their communications applications.
3/4/2024 22:49 Mastering Efficient Array Operations with StaticArrays.jl in Julia: Good tutorial with benchmarks.
21/2/2024 7:12 New stack traces in Pluto: more colorful, less intimidating!: I recently wrote about the new features in Julia v1.10, including the friendlier, more useful stacktraces in the REPL. Now the amazing Fons van der Plas has released a new version of Pluto, the reactive computational notebook for Julia, with powerful and helpful stacktraces inspired by the new REPL improvements—they take error reporting to a new level.
5/1/2024 19:57 Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1): “In the end, we were impressed by our experience with Julia. It did fulfill the promise of fast and convenient prototyping, with the ability to eventually run at high speeds on multiple high-performance architectures – after some effort and lessons learned by the developers.”
3/1/2024 17:23 Julia v1.10 Release Notes: Excellent progress, especially in package loading times. Watch for my LWN writeup, coming soon.
03/01/2024 14:40 Julia v1.10: Performance, a new parser, and more: My article about the Julia v1.10 release appeared today in LWN.
19/11/2023 7:30 Julia as a unifying end-to-end workflow language on the Frontier exascale system: “Julia emerges as a compelling high-performance and high-productivity workflow composition language, as measured on the fastest supercomputer in the world.”
25/10/2023 12:32 Practical Julia: My introduction to the best language for scientific computing.
25/7/2023 10:11 JuliaCon 2023 Live Streaming: Some of the program of this year’s Julia conference will be streamed on YouTube, free to watch.
16/7/2023 8:18 My approach to named tuples in Julia: “I find it more natural to think of a NamedTuple as an anonymous struct, that optionally can be integer-indexed to get access to its components.”
15/7/2023 11:26 PSA: Thread-local state is no longer recommended: “Any code that relies on a specific threadid staying constant, or on a constant number of threads during execution, is bound to be incorrect.”
7/6/2023 8:28 Comparison of plotting packages: Newcomers (and others!) using Julia can be bewildered by the profusion of options for making plots and visualizations. Chris Rackauckas provides a useful summary of the pros and cons of all the major packages.
7/6/2023 7:59 Potential of the Julia programming language for high energy physics computing: “In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language.”
7/6/2023 7:35 DynamicQuantities.jl: Type stable units in Julia: Along the lines of Unitful, but storing dimensions as values rather than as parametric types. This leads to greater efficiency in many circumstances.
7/6/2023 7:07 Filtering grouped data frames in DataFrames.jl: A helpful tutorial by Bogumił Kamiński.
29/5/2023 20:58 Julia 1.9 Brings More Speed and Convenience: My article about the goodies in Julia v.1.9 appeared today in LWN.

Documentation and tutorials

Particle Simulations with Julia: Excellent tutorial and demonstration available as a web page and as a Pluto notebook. Contains fascinating animations of error propagation in a running simulation.
Julia: The Greeks and Math operators at the command line via LaTeX: If you haven’t tried Julia, this may whet your appetite.
Setting up your Julia session: A tutorial about the Julia startup file.
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications: “This book is a compilation of lecture notes from the MIT Course 18.337J/6.338J: Parallel Computing and Scientific Machine Learning…a live document, updating to continuously add the latest details on methods from the field of scientific machine learning and the latest techniques for high-performance computing.”
Learning to create a vector in Julia: Two ways to turn a multidimensional array into a Vector in Julia: one that makes a copy and one that uses an alias.
Type-Dispatch Design: Post Object-Oriented Programming for Julia: “I am going to try to explain in detail the type-dispatch design which is used in Julian software architectures. It’s modeled after the design of many different packages and Julia Base […] Julia is built around types. Software architectures in Julia are built around good use of the type system. This makes it easy to build generic code which works over a large range of types and gets good performance. The result is high-performance code that has many features. In fact, with generic typing, your code may have more features than you know of! The purpose of this tutorial is to introduce the multiple dispatch designs that allow this to happen.”
What does it mean that a data frame is a collection of rows?: The main developer of Dataframes.jl explains its interface in this illuminating article.
Differentiation for Hackers: “The goal of this handbook is to demystify algorithmic differentiation, the tool that underlies modern machine learning.”
Julia Books: A list from Julia headquarters of works available now or on the way.
Successful Static Compilation of Julia Code for use in Production: “a successful experiment to statically compile a piece of Julia code into a small .so library on Linux, which is then loaded from Python and used in training of a deep learning model.”
DataFrame vs NamedTuple: a comparison: Informative comparison of the use of dataframes vs. named tuples to represent tables in Julia, with interesting examples of fast and slow code.
Julia Data Science: A free and open-source book in several formats, including a paperback on amazon. Confined mainly to a brief language introduction, dataframes, and plotting with Mackie.
PlutoUI Demonstration: A live demonstration of PlutoUI controls, served with the PlutoSliderServer.
Julia macros for beginners: A clear introduction.
Pkg.jl and Julia Environments for Beginners: Exceptionally clear tutorial about Julia’s package manager.
Bayesian Estimation of Differential Equations: Tutorials showing how to use Turing.jl to estimate parameters of ODEs from data.
Fundamentals of Numerical Computation: “This is a revision of the textbook Fundamentals of Numerical Computation by Tobin A. Driscoll and Richard J. Braun. The book was originally written for MATLAB, but this resource has been adapted to suit Julia.” Text available free online.
Storing vectors of vectors in DataFrames.jl: Extremely useful tutorial on preventing vectors from being expanded into multiple rows when applying transformations to dataframes.
Ref ref: “Today I want to discuss the Ref type defined in Base Julia. The reason is that it is often used in practice, but it is not immediately obvious what the design behind Ref is.”
Functional One-Liners in Julia: Introduction to currying used in conjunction with broadcasting, with examples.
Triangle frenzy: A case study using CUDA for some graphics calculations.
Practical guide: how to contribute to open source Julia projects: This is helpful for those interested in making their first contribution to the ecosystem.
Python Criticism from a Julia Perspective: Contains, among other interesting nuggets, a description of a Julia REPL facility for discovering a list of methods that can act on a given combination of types (I had no idea).
Using Julia on the HPC: A survey of simple examples of using Julia in HPC contexts.
Metadata in DataFrames.jl: why and how?: Valuable and detailed introduction to this useful feature of DataFrames.jl, by one of its developers and the author of Julia for Data Analysis.
GPU Programming: When, Why and How?: This course on using GPUs in general computing contains a section (“High-level language support”) with an overview of Julia and Python.
Guide for writing shell scripts in Julia: From 2019, but may still be useful.
My understanding of object property access in Julia: Clarifies the use of fields and properties of composite objects.


TensorCast.jl: It slices, it dices, it splices!: “This package lets you work with multi-dimensional arrays in index notation, by defining a few macros which translate this to broadcasting, permuting, and reducing operations.”
SpeedyWeather: “a global spectral atmospheric model with simple physics which is developed as a research playground with an everything-flexible attitude as long as it is speedy…Why another model? You may ask. We believe that most currently available are stiff, difficult to use and extend, and therefore slow down research whereas a modern code in a modern language wouldn’t have to.”
Gnuplot.jl: Julia interface to gnuplot: This is a very well-thought-out way to use gnuplot from Julia (you need gnuplot installed), giving you the best of both worlds. For (the excellent) documentation, go to the docs/src folder and browse the .md files, starting with
Changing Physics education with Julia: Interactivity in physics education; why Julia is an ideal language for physics education; applied to teaching dynamical systems and the DynamicalSystems library, of which the author is the maintainer, and his textbook.
My Julia Book is in Libraries: giving me a warm feeling.
JuliaHEP: High energy physics packages. Includes compatibility layers and partial replacements for ROOT.
Yao: “Welcome to Yao, a Flexible, Extensible, Efficient Framework for Quantum Algorithm Design. Yao (幺) is the Chinese character for normalized but not orthogonal.”
SuiteSparseGraphBLAS.jl: An Introduction: “This blog post serves a couple purposes. The first is to introduce Julia users to GraphBLAS, and the features and performance of the SuiteSparseGraphBLAS.jl package. The second is an update on new features in versions 0.6 and 0.7 as well as a roadmap for the near future.”
Here’s why quantum computing could be the big break for the Julia Language: Introducing Yao.jl, a Julia library for quantum computing.
Books.jl: Creates books with embedded output from Julia code, using Pandoc.
ThreadPools: A set of macros for concurrency. Last updated around 2021; some of its function is provided by the new interactive task feature in Julia 1.9.
Stipple: A reactive user interface library for use with Genie. Standard use is over a websocket, somewhat similarly to Phoenix Liveview. Seems quite useful for building interactive web applications with Julia, but Unfortunately undocumented, like most Julia packages.
Introducing Braket.jl - Quantum Computing with Julia: “Braket.jl is a new, experimental package that lets you build quantum circuits and programs in Julia and run them on Amazon Braket.”
PlutoSliderServer.jl: Web server to run just the @bind parts of a Pluto.jl notebook.
Microstructure.jl: “Microstructure.jl is a Julia package for fast and probabilistic microstructure imaging… under active development…ocuses on tissue microstructure estimation.”
ImplicitPlots.jl: Julia package for the plotting of plane curves and surfaces: “Plot plane curves defined by an implicit function f(x,y)=0.”
WaterLily.jl: Fast and simple fluid simulator in Julia: “WaterLily.jl solves the unsteady incompressible 2D or 3D Navier-Stokes equations on a Cartesian grid. The pressure Poisson equation is solved with a geometric multigrid method. Solid boundaries are modelled using the Boundary Data Immersion Method.”
Pluto: The computational notebook for Julia.
Tidier.jl: “Tidier.jl is a data analysis package inspired by R’s tidyverse and crafted specifically for Julia.”
PlanetOrbits.jl: Tools for solving and displaying Keplerian orbits for exoplanets.: “The primary use case is mapping Keplerian orbital elements into Cartesian coordinates at different times. A Plots.jl recipe is included for easily plotting orbits.”
JuliaSymbolics: “JuliaSymbolics is the Julia organization dedicated to building a fully-featured and high performance Computer Algebra System (CAS) for the Julia programming language.”
Handcalcs.jl: “This package supplies macros to generate LaTeX formatted strings from math[e]matical formulas.” For use in Pluto.
FunSQL.jl: Julia library for compositional construction of SQL queries’
PureFun.jl: Immutable containers for Julia.
KittyTerminalImages: If you use the superb Kitty terminal emulator, this package is for you. It lets Julia display images directly in the terminal, using the Kitty protocol (not sixel, etc.).
Merly.jl: Micro framework for web programming in Julia: Seems similar to Oxygen, but hasn’t been updated since about 2020.
Julia library for stylized terminal output: Create beautiful output in the terminal.
GridWorlds.jl: Help! I’m lost in the flatland!: “A package for creating grid world environments for reinforcement learning in Julia.“ Juliacon presentation here.
Pandoc.jl: Pandoc Interface and Types in Julia: Run Pandoc and write Pandoc filters in Julia. Uses the json serialization, so won’t be as fast as Lua filters.
Trixi.jl: “Trixi.jl is a numerical simulation framework for conservation laws written in Julia. A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures.”
Strided.jl: Make broadcast operations faster.
JuliaAstroSim/AstroNbodySim.jl: “Unitful and differentiable gravitational N-body simulation code in Julia”.
PairPlots.jl: Julia package for making corner plots, the statistical visualization that lets you compare a collection of distributions all at once.
TikzGraphs: Exploiting the superb TikZ system from Julia to create high-quality graph diagrams (“graph” in the node-edge sense) conveniently.
JuliaFolds/Folds.jl: A unified interface for sequential, threaded, and distributed fold: Powerful, convenient, does what it’s supposed to do.
Oxygen.jl: A breath of fresh air for programming web apps in Julia: A micro-framework that provides routing and request/response handling.


The best Julia programming books going into 2023: Description of books the author has used plus notes about others, including books arriving in the near future.
Nonlinear Dynamics: “A Concise Introduction Interlaced with Code”.
Practical Julia: The publisher’s page for my Julia book.
Composability in Julia: Implementing Deep Equilibrium Models via Neural ODEs: “The SciML Common Interface defines a complete set of equation solving techniques, from differential equations and optimization to nonlinear solves and integration (quadrature), in a way that is made to naturally mix with machine learning. In this sense, there is no difference between the optimized libraries being used for physical modeling and the techniques used in machine learning: in the composable ecosystem of Julia, these are one and the same.[…] With a composable package ecosystem, the only thing holding you back is the ability to figure out new ways to compose the parts.”
Julia 1.8 released: I’ve been using the betas for a while and they’ve been stable and responsive. The highlights in 1.8 are typed non-constant globals and improvements in precompilation, both significant for all users.
Solving one based indexing: Finally, a solution to the non-problem of 1-based indexing in Julia that should satisfy everybody.
Automated Code Optimization with E-Graphs: “Can programmers implement their own high-level compiler optimizations for their domain-specific scientific programs, without the requirement of them being compiler experts at all? […] can symbolic mathematics do high-level compiler optimizations or vice-versa?”
Comparing Julia to Performance Portable Parallel Programming Models for HPC: “Julia programs enjoys comparable performance to native C/C++ solutions.[…] Julia gets the unique opportunity to provide best-in-class performance on some of the latest hardware platforms.”
A Look at Communication-Intensive Performance in Julia: “if programmers properly balance the computation to communication ratio, Julia can actually outperform C/MPI in a cluster computing environment.”
Why Julia is the most suitable language for science: Engaging presentation by the maintainer of the Julia dynamical systems package.
What’s great about Julia?: The other side of the coin revealed by the author of “What’s bad about Julia?”.
Macroeconomic Modeling: “The Federal Reserve Bank of New York publishes its trademark Dynamic Stochastic General Equilibrium models in Julia.”
Meshes.jl: Computational geometry and meshing algorithms in Julia
JuliaDiff: Mainly a big list of differentiation tools in Julia.
Julia 1.9 Highlights: Julia v.1.9 is now officially released. I’ve been enjoying the 1.9 series since the beta releases. The new version offers significant performance improvements and is even more convenient for development.
Concurrency in Julia: An overview of the easiest-to-approach facilities for parallel and concurrent computation in Julia, with a demonstration of the effects of a new feature: thread migration. Discussed on Hacker News.
Extreme Multi-Threading: C++ and Julia 1.9 Integration: With Julia v.1.9 we can call Julia libraries from multi-threaded C++ programs, apparently. This article shows you how.
State of machine learning in Julia: …as of Jan, 2022.
Bash shell completions for Julia: Bash shell completions contextually finish what you’re typing when you hit the tab key. This project tells the shell about Julia commands, to save you keystrokes.
Switching from Common Lisp to Julia: Although written in the pre-1.0 Julia era, the author penetrates into the core of Julia’s uniqueness: “A sufficiently rich parametric type system with multiple dispatch integrated into the language and supported by a JIT compiler is the secret weapon of Julia.” An informative discussion of scientific computing in CL and Julia.
Fermi.jl: A Modern Design for Quantum Chemistry: “we introduce the quantum chemistry package Fermi.jl, which contains the first implementations of post-Hartree–Fock methods written in Julia. Its design makes use of many Julia core features, including multiple dispatch, metaprogramming, and interactive usage. Fermi.jl is a modular package, where new methods and implementations can be easily added to the existing code. Furthermore, it is designed to maximize code reusability by relying on general functions with specialized methods for particular cases.”
Pluto 0.17.6 Announced: The latest version of Pluto, the computational notebook for Julia, has some great new features, including a dark mode and the ability to print logging messages from running code in the notebook.
Julia v1.8 Release Notes: Julia 1.8.0 is in beta. These release notes include many welcome enhancements. Several of the new features relate to the creation of sysimages, and appear to make it easier to produce smaller compiled code.
Bridging HPC Communities through the Julia Programming Language: “The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications.”
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models: Data-driven Physical Simulations Seminar Series talk by Chris Rackauckas.
Why not: from Common Lisp to Julia: Somewhat of a rebuttal to this.
Julia in the Wild: Examples: Case studies of Julia use in science, engineering, industry, and finance.
Julia: Project Workflow: Good ideas about organizing Julia projects, including how to incorporate Pluto notebooks.
Jack Dongarra, who made supercomputers usable, awarded 2021 ACM Turing prize: The creative force behind LINPACK and BLAS says that Julia is a good candidate for the next computing paradigm.
The Essential Tools of Scientific Machine Learning: An overview of the state of packages and libraries for scientific ML as of 2019, not confined to Julia.
Modern Julia Workflows: “Our purpose is to gather the hidden tips and tricks of Julia development, and make them easily accessible to beginners. We do not cover syntax, and assume that the reader is familiar enough with the basics of Julia. Instead, we focus on all the tools that can make the coding experience more pleasant and efficient.”
Femtolisp: a lightweight, robust, scheme-like lisp implementation: The Julia parser up to Julia v1.10. “This project began with an attempt to write the fastest lisp interpreter I could in under 1000 lines of C.”
2021_FortranCon Benchmarks: Comparison of of code and performance between Julia and Fortran on a 2D particle simulation.
Review of Interactive Visualization and Plotting with Julia by Zea: A knowledgeable author poorly served by a careless publisher.
Oceananigans.jl: Fast and friendly geophysical fluid dynamics on GPUs: “Oceananigans.jl is a fast and friendly software package for the numerical simulation of incompressible, stratified, rotating fluid flows on CPUs and GPUs. Oceananigans.jl is fast and flexible enough for research yet simple enough for students and first-time programmers. Oceananigans.jl is being developed as part of the Climate Modeling Alliance project for the simulation of small-scale ocean physics at high-resolution that affect the evolution of Earth’s climate.”
From Common Lisp to Julia: A personal journey from CL to Julia from a graphics and games programmer. Interesting observations, badly in need of editing.
Julia: The Goldilocks language: Contains some interesting details about the origins and history of Julia, along with some odd remarks (“elements of the high-level functionality of MATLAB and R with the speed of C or Ruby”).
C. Brenhin Keller: An assistant Professor of Earth Sciences at Dartmouth using Julia in research and teaching. Many links to Julia Earth science resources.
Threadpools in Julia: My favorite new feature in Julia v.1.9: spawn as many tasks as you want, but keep a thread reserved for the REPL, so it continues to respond instantly.
Switching from Common Lisp to Julia: “A sufficiently rich parametric type system with multiple dispatch integrated into the language and supported by a JIT compiler is the secret weapon of Julia” and the core reason for the author’s adoption of the language for scientific computing.
Digging into Julia’s package system: How Julia’s excellent package system eases development and code sharing.
What’s the deal with Julia binary sizes?: Traces the huge decreases in the sizes of compiled binaries.
Julia REPL ↓ trick: Stefan Karpinski reveals a secret superpower in plain sight: “After evaluating a command from history in the REPL, you can press ↓ and get the next command after that one, so you can replay a sequence of commands by finding the first one and doing ↓⏎ repeatedly”.
Julia for Biologists: “Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages—Julia—is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia’s design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.”
Inviscid and incompressible Navier-Stokes equations in 2D implemented in Julia and compiled to WASM: This is quite wonderful and amazing: a Julia fluid solver compiled to WASM and running in the browser. Users can create flow boundaries with the mouse.

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