Julia
This is a list of resources and articles about the Julia language.
Jump to: Documentation • Libraries • Other
News
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 Back from the Printer: My introduction to the best language for scientific computing has achieved physical form.
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.
16/5/2023 8:00 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.
13/5/2023 12:52 Julia 1.9.0 lives up to its promise
12/5/2023 6:31 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.
10/5/2023 7:00 Julia 1.9 Released: 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.
14/4/2023 10:45 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.
06/04/2023 21:21 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.”
2/4/2023 10:30 Solving one based indexing: Finally, a solution to the non-problem of 1-based indexing in Julia that should satisfy everybody.
1/4/2023 7:59 Julia’s latency: Past, present and future
18/3/2023 10:19 Practical guide: how to contribute to open source Julia projects: This is helpful for those interested in making their first contribution to the ecosystem.
3/12/2022 12:38 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.
Documentation and tutorials
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.”
HPC Cafe: An introduction to the Julia programming language for HPC: This video is packed with insights about high performance computing with Julia, and is easy to follow.
Triangle frenzy: A case study using CUDA for some graphics calculations.
Julia Books: A list from Julia headquarters of works available now or on the way.
PlutoUI Demonstration: A live demonstration of PlutoUI controls, served with the PlutoSliderServer.
Julia macros for beginners: A clear introduction.
Writing type-stable Julia code: Using many examples, this tutorial will help you understand how to do what’s in the title.
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.
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.”
What is a Symbol in Julia?: Stefan Karpinski’s masterful explanation of what a Symbol really is.
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.”
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.
Convolutions in image processing: Not much about Julia, but a superb demonstration of how simple convolutions can create blurring, sharpening, and edge detection. Ends by showing how to speed up the calculation using FFTs.
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.
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.
Guide for writing shell scripts in Julia: From 2019, but may still be useful.
Bayesian Estimation of Differential Equations: Tutorials showing how to use Turing.jl to estimate parameters of ODEs from data.
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.
Signal Processing with Julia Language: A tutorial series.
Functional One-Liners in Julia: Introduction to currying used in conjunction with broadcasting, with examples.
Differentiation for Hackers: “The goal of this handbook is to demystify algorithmic differentiation, the tool that underlies modern machine learning.”
Julia Meta Types: “One of my favorite tools and a double edged blade in the Julia programming language are Meta Types. Loosely related to Metaprogramming, meta types, derived from the Greek word μετα, ‘transcend’ beyond a simple data structure. Rather than simply describe a physical data structure in memory, a meta type includes information on the type itself.”
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.
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.
Julia: The Greeks and Math operators at the command line via LaTeX: If you haven’t tried Julia, this may whet your appetite.
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.”
Libraries
JuliaHEP: High energy physics packages. Includes compatibility layers and partial replacements for ROOT.
Agents.jl: A Julia package for agent-based modeling. Very easy to set up simulations on a variety of spaces, including Open Street Map.
JuliaFolds/Folds.jl: A unified interface for sequential, threaded, and distributed fold: Powerful, convenient, does what it’s supposed to do.
PairPlots.jl: Julia package for making corner plots, the statistical visualization that lets you compare a collection of distributions all at once.
PlutoSliderServer.jl: Web server to run just the
@bind parts of a Pluto.jl notebook.
SciML: Differentiable Modeling and Simulation Combined with Machine Learning: A new hub for Julia’s SciML documentation.
ImageClipboard.jl: Copy & Paste images with Julia
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.”
PureFun.jl: Immutable containers for Julia.
Books.jl: Creates books with embedded output from Julia code, using Pandoc.
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.”
Bayesian Statistics using Julia and Turing: “Welcome to the repository of tutorials on how to do Bayesian Statistics using Julia and Turing.”
Pluto’s built-in package management: “Starting with version 0.15, Pluto has a built-in package manager, which means:
- Packages are automatically installed when you use import or using.
- Your package environment is stored inside the notebook file. When someone else opens your notebook with Pluto, the exact same package environment will be used, and packages will work on their computer.
FunSQL.jl: Julia library for compositional construction of SQL queries’
Oxygen.jl: A breath of fresh air for programming web apps in Julia: A micro-framework that provides routing and request/response handling.
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.
APL.jl: APL in Julia, with all the beautiful characters, and with Julia’s speed.
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.”
Turing.jl: “Bayesian inference with probabilistic programming.”
Swalbe.jl: A lattice Boltzmann solver for thin film hydrodynamics: A Julia program.
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.”
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.
JuliaAstroSim/AstroNbodySim.jl: “Unitful and differentiable gravitational N-body simulation code in Julia”.
Here’s why quantum computing could be the big break for the Julia Language: Introducing Yao.jl, a Julia library for quantum computing.
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.
EmacsVterm.jl: “Better integration of Julia REPL with Emacs vterm terminal.”
TikzGraphs: Exploiting the superb TikZ system from Julia to create high-quality graph diagrams (“graph” in the node-edge sense) conveniently.
Merly.jl: Micro framework for web programming in Julia: Seems similar to Oxygen, but hasn’t been updated since about 2020.
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.
ImplicitPlots.jl: Julia package for the plotting of plane curves and surfaces: “Plot plane curves defined by an implicit function f(x,y)=0.”
GridWorlds.jl: Help! I’m lost in the flatland!: “A package for creating grid world environments for reinforcement learning in Julia.“ Juliacon presentation here.
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.”
Julia library for stylized terminal output: Create beautiful output in the terminal.
JuliaSymbolics: “JuliaSymbolics is the Julia organization dedicated to building a fully-featured and high performance Computer Algebra System (CAS) for the Julia programming language.”
Other
State of machine learning in Julia: …as of Jan, 2022.
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.”
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.”
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.
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”.
Macroeconomic Modeling: “The Federal Reserve Bank of New York publishes its trademark Dynamic Stochastic General Equilibrium models in Julia.”
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.
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.
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.
Nonlinear Dynamics: “A Concise Introduction Interlaced with Code”.
Julia v1.7.2 Released: “As a patch release, 1.7.2 contains no new features or breaking changes, only bug fixes, documentation improvements, and performance improvements. […] We recommend that anyone currently using 1.7.0 or 1.7.1 upgrade to 1.7.2. Note that 1.7 on Travis, AppVeyor, Cirrus, and GitHub Actions now refers to 1.7.2.”
What’s great about Julia?: The other side of the coin revealed by the author of “What’s bad about Julia?”.
Julia: Project Workflow: Good ideas about organizing Julia projects, including how to incorporate Pluto notebooks.
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.
C. Brenhin Keller: An assistant Professor of Earth Sciences at Dartmouth using Julia in research and teaching. Many links to Julia Earth science resources.
Digging into Julia’s package system: How Julia’s excellent package system eases development and code sharing.
What’s bad about Julia?: A sophisticated and detailed critique, concentrating especially on the type system, from the point of view of a Julia user.
ModelingToolkit.jl: “A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations”.
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models: Data-driven Physical Simulations Seminar Series talk by Chris Rackauckas.
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.
Greenspunning Predicate and Multiple Dispatch in JavaScript: Nothing to do with Julia, but contains a clear discussion of the expression problem.
What’s coming in Julia 1.7: New features on the way.
GitHub - JuliaDynamics/NonlinearDynamicsTextbook: coming soon: A repository for the book Nonlinear Dynamics.
Magnification Shape Styling in Lens Insets in Julia: In the current version of
Plots you can style the lines with which the magnification shape is drawn when making lens insets.
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.
Julia 1.7 Highlights: “After 4 betas and 3 release candidates, Julia version 1.7 has finally been released.”
optimizer: supports callsite annotations of inlining: fixes #18773
Auto-Optimization and Parallelism in DifferentialEquations.jl: A video by Chris Rackauckas describing two levels of automation for the scientist who needs to solve some differential equations efficiently: first, detect properties of the equation system to choose the best solver; but also automate the choice of what algorithm use to do that. The pieces are under the SciML ecosystem.
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?”
Review of Interactive Visualization and Plotting with Julia by Zea: A knowledgeable author poorly served by a careless publisher.
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.
Julia v.1.6 release addresses latency issues: Julia’s new release turns the “time to first plot” problem into (almost) a non-issue.
Julia’s Big Problem With Namespace: Points out how
using two modules that export the same names leads to a naming conflict.
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.”
Analyzing sources of compiler latency in Julia: method invalidations: “The Julia programming language has wonderful flexibility with types, and this allows you to combine packages in unanticipated ways to solve new kinds of problems. Crucially, it achieves this flexibility without sacrificing runtime performance. It does this by running versions of code or algorithms that have been”specialized” for the specific types you are using. Creating these specializations is compilation, and when done on the fly (as is common in Julia) this is called “just in time” (JIT) compilation. Unfortunately, JIT-compilation takes time, and this contributes to latency when you first run Julia code. This problem is often summarized as “time-to-first-plot,” though there is nothing specific about plotting other than the fact that plotting libraries tend to involve large code bases, and these must be JIT-compiled.”
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.
Struct Destructuring in Julia: The soon-to-arrive Julia v.1.7 has a new destructuring syntax for structs. I take a look at what it’s good for.
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.
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.”
Computer algebra in Julia: A survey.
Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology: Fire forecasting using Flux.jl.
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.”
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.
Meshes.jl: Computational geometry and meshing algorithms in Julia
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.
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”).
Julia in the Wild: Examples: Case studies of Julia use in science, engineering, industry, and finance.
Julia Computing Raises $24M Series A: “Julia Computing will use the funding to further develop and advance its secure, high-performance JuliaHub cloud platform and to grow the Julia ecosystem.”
2021_FortranCon Benchmarks: Comparison of of code and performance between Julia and Fortran on a 2D particle simulation.
