Julia

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

News

19/5/2020 21:49 OSCAR Computer Algebra System: A symbolic mathematics library for Julia, in an early stage of development.
10/5/2020 10:05 Turing.jl: “Bayesian inference with probabilistic programming”—a Julia package written in Julia.
5/5/2020 12:41 Coming in Julia 1.5: Time Traveling (Linux) Bug Reporting: “If you can reproduce it on a linux machine and get us a trace from the rr tool https://rr-project.org/, we can probably get it fixed for you very rapidly. For the uninitiated, rr is a Linux debugging tool originally developed at Mozilla by Robert O’Callahan and others. It is a tool known as a ‘time traveling debugger’ or ‘reverse execution engine’.”
10/4/2020 18:15 Announcing Gnuplot v1.1.0 - A Julia interface to gnuplot: “the first production ready version of Gnuplot.jl, a package to drive an external gnuplot 16 process from Julia.”
9/8/2018 12:46 Julia 1.0 is here: Back in 2014 I thought that Julia might become the preferred language for numerically intensive computing. It’s already made impressive inroads in this area, and the arrival of v.1.0, with its promise of a long future of non-breaking language development, can only accelerate its adoption.
1/6/2018 14:46 Julia Keeps Getting Better: As release 1.0 of the numerical/scientific language Julia was approaching, here was one man’s list of favorite new features.
11/11/2015 12:51 Great News for the Julia Language: A $600,000 grant to help get to the 1.0 release.
15/9/2014 22:40 Vectorization with Julia: Detailed article on SIMD vectorization in Julia 0.3.
21/8/2014 20:06 Julia 0.3 Released: The latest release contains numerous performance, library, and REPL enhancements.
11/7/2014 16:34 Web development in Julia: A progress report.: First steps [2014].
7/7/2014 14:07 GraphLayout.jl: “Graph layout algorithms in pure Julia. Currently only has the spring-based method of Fruchterman and Reingold (1991), but more can and will be added.”
1/7/2014 10:06 JuliaCon Presentation Slides: The Julia language’s first conference has recently wound up and the PDFs of the slide decks are available here. Just click on the topic you want to see and then on the filename to download (from 2014).

Documentation and tutorials

Julia By Example: Learn basic Julia syntax by example, plus four plotting packages and a taste of DataFrames.
Fast as Fortran, Easy as Python: My introduction to Julia in Linux Pro Magazine.
Julia: Learn the New Language for Scientific Computing: My introduction to Julia in Linux Format, available for subscribers to the magazine.
Six Months With Julia: Parse-time Transpilation in 80 Lines or Less: A case study in applying Julia’s string macros to allow it to read a different language.
Julia 1.0 Documentation: The official source.
Julia, Matlab, R, Python Cheatsheat: Convenient table comparing (mostly) matrix operations among these languages (left out APL).
Julia Express: Summary of Julia syntax (pdf). Updated: 12Aug2018.
Metaprogramming in Julia: A Jupyter notebook showing some numerical applications of macros.
Multi-node Parallelism in Julia on an HPC (XSEDE Comet): “Today I am going to show you how to parallelize your Julia code over some standard HPC interfaces.”
Julia for Python Programmers: Although Julia is not very close to Python, this might be a useful reference for a Python programmer beginning the upgrade process.
Using ASCIIPlots.jl: Julia library for plots in the style of gnuplot’s “dumb terminal”

Other

High Performance Technical Computing in Dynamic Languages: Conference held in conjunction with SC14: The International Conference for High Performance Computing, Networking, Storage and Analysis.
Julia Is Awesome, But…: Dan Luu finds a plethora of bugs in the Julia core, and blames it on a lack of testing discipline.
Nemo: A computer algebra package for the Julia programming language.
Tricks in Julia: Erik Engheim: “After doing various small Julia projects I’ve had to learn a number of tricks or solutions to small problems.”
A Julia interpreter and debugger: A major advance in tooling for Julia.
Hacker News Julia discussion: There is some discussion of Julia and my LWN article on the Hacker News front page today.
Modular Algorithms for Scientific Computing in Julia: “It allows you to design an algorithm in a very generic form, essentially writing your full package with inputs saying ‘insert scientific computing package here’, allowing users to specialize the entire overarching algorithm to the specific problem. JuliaDiffEq, JuliaML, JuliaOpt, and JuliaPlots have all be making use of this style”.
IJulia.jl: The julia Jupyter kernel.
These are a few of my Favourite Things (that are coming with Julia 1.0): As release 1.0 of the numerical/scientific language Julia was approaching, here was one man’s list of favorite new features.
Parallel Supercomputing for Astronomy: “The Celeste research team spent three years developing and testing a new parallel computing method that was used to process the Sloan Digital Sky Survey dataset and produce the most accurate catalog of 188 million astronomical objects in just 14.6 minutes with state-of-the-art point and uncertainty estimates.”
Julia Observer: Explore the Julia package registry.
Julia 1.0 is here: Back in 2014 I thought that Julia might become the preferred language for numerically intensive computing. It’s already made impressive inroads in this area, and the arrival of v.1.0, with its promise of a long future of non-breaking language development, can only accelerate its adoption.
JuliaCon Presentation Slides: The Julia language’s first conference has recently wound up and the PDFs of the slide decks are available here. Just click on the topic you want to see and then on the filename to download (from 2014).
Plots - powerful convenience for visualization in Julia: “Plots is a visualization interface and toolset. It sits above other backends, like GR or PyPlot, connecting commands with implementation. If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn.”
Julialab: Julia Language Research and Development at MIT
An introduction to the Julia language, part 1: My article on the rapidly growing language for scientific and technical (and other kinds of) computing in LWN. Please take a moment to subscribe while you’re there.
I ❤ Julia: Some great things about the Julia language.
PyCall.jl: “Package to call Python functions from the Julia language.”
Julia By Example: Learn basic Julia syntax by example, plus four plotting packages and a taste of DataFrames.
An Endorsement of Julia for Scientific Computing: Sebastian Nowozin finds the Julia language highly productive. The most serious obstacle now seems to be the lack of single-machine parallelism.
Julia’s Role in Data Science: Brief, down-to-earth assessment of the current state of the language and ecosystem.
Great News for the Julia Language: A $600,000 grant to help get to the 1.0 release.
Vectorization with Julia: Detailed article on SIMD vectorization in Julia 0.3.
Julia: A Fresh Approach to Numerical Computing: A foundational article about the language design.
PyPlot: An interface to Python’s Matplotlib.
Fun With Just-In-Time Compiling: Julia, Python, R and pqR: A quick numerical benchmark comparing Julia with various implementations of R and Python.
Julia 0.3 Released: The latest release contains numerous performance, library, and REPL enhancements.
GraphLayout.jl: “Graph layout algorithms in pure Julia. Currently only has the spring-based method of Fruchterman and Reingold (1991), but more can and will be added.”
Julia: A fresh approach to numerical computing: An article by the creators of Julia.
The Julia Language: The official page.
Winston: 2D plotting: “Winston offers an easy to use plot command to create figures without any fuss.”
Gadfly: “Gadfly is a plotting and data visualization system […] influenced heavily by Leland Wilkinson’s book The Grammar of Graphics and Hadley Wickham’s refinment of that grammar in ggplot2.”
More Dots - Syntactic Loop Fusion in Julia: Impressive progress on efficiency of automatically vectorized loops using an expressive syntax.
Juno, the Interactive Development Environment for Julia: Built on top of LightTable, and looks very slick.
The Design Impact of Multiple Dispatch: …As the core paradigm of Julia. Presented at Strange Loop on September 19, 2013 by Stefan Karpinski.
Plotting Probability Distributions: Tries out several plotting packages.
LaTeX symbols in Julia REPL: The Julia language lets you use Unicode symbols – but how do you enter them in the REPL? Here is a patch that lets you enter them using LaTeX syntax.
Julia for Python Programmers: Although Julia is not very close to Python, this might be a useful reference for a Python programmer beginning the upgrade process.
Escher: Webserving with Julia: “With Escher you can build beautiful Web UIs entirely in Julia.” Includes a built-in webserver.
Linear Algebra in Julia: A personal exploration that serves as a brief introduction to the subject.
Six Months With Julia: Parse-time Transpilation in 80 Lines or Less: A case study in applying Julia’s string macros to allow it to read a different language.
Announcing Gnuplot v1.1.0 - A Julia interface to gnuplot: “the first production ready version of Gnuplot.jl, a package to drive an external gnuplot 16 process from Julia.”
Jupyter, Mathematica, and the Future of the Research Paper: Paul Romer shares, in a subtly written article, an instructive anecdote contrasting his experiences with proprietary vs. open source software. He is able to explain why Jupyter has taken over from Mathematica’s early lead.
Why I’m Betting on Julia: Evan Miller’s personal take on the unique, upstart scientific computing language.
Scientific computing’s future: Check out my article in Ars Technica about Fortran, Julia, Clojure, and Haskell.
A Lisper’s first impression of Julia: A quite detailed and opinionated comparison of the various ways in which Julia is better or worse than Common Lisp.
Wired article about the Julia language: There is growing excitement about this fairly new language in scientific computing circles and elsewhere. It’s nice to see some high-profile coverage in an article that traces its origins and potential. (Not everyone is happy with this article.)
Coming in Julia 1.5: Time Traveling (Linux) Bug Reporting: “If you can reproduce it on a linux machine and get us a trace from the rr tool https://rr-project.org/, we can probably get it fixed for you very rapidly. For the uninitiated, rr is a Linux debugging tool originally developed at Mozilla by Robert O’Callahan and others. It is a tool known as a ‘time traveling debugger’ or ‘reverse execution engine’.”
Open Big Data Computing with Julia: Intel Science & Technology Center for Big Data
Why I Switched to Julia: A case study of Julia used in econometrics that shows 100-fold speed increase over Python with Numpy.
Julia: A Fast Language for Numerical Computing: Julia may be the eventual successor to Fortran for high-performance scientific computing.

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