The Julia Language
Julia is a high-level, high-performance dynamic language for technical computing. The main homepage for Julia can be found at julialang.org. This is the GitHub repository of Julia source code, including instructions for compiling and installing Julia, below.
- Homepage: http://julialang.org
- Binaries: http://julialang.org/downloads/
- Documentation: http://docs.julialang.org/
- Packages: http://pkg.julialang.org/
- Source code: https://github.com/JuliaLang/julia
- Git clone URL: git://github.com/JuliaLang/julia.git
- Mailing lists: http://julialang.org/community/
- IRC: http://webchat.freenode.net/?channels=julia
The mailing list for developer discussion is http://groups.google.com/group/julia-dev/. All are welcome, but the volume of messages is higher, and the discussions tend to be more esoteric. New developers may find the notes in CONTRIBUTING helpful to start contributing to the Julia codebase.
Currently Supported Platforms
- Darwin/OS X
All systems are supported with both x86/64 (64-bit) and x86 (32-bit) architectures.
Source Download and Compilation
First, acquire the source code by cloning the git repository:
git clone git://github.com/JuliaLang/julia.git
(If you are behind a firewall, you may need to use the
https protocol instead of the
git config --global url."https://".insteadOf git://
Be sure to also configure your system to use the appropriate proxy settings, e.g. by setting the
Next, enter the
julia/ directory and run
make to build the
julia executable. To perform a parallel build, use
make -j N and supply the maximum number of concurrent processes.
When compiled the first time, it will automatically download and build its external dependencies.
This takes a while, but only has to be done once. If the defaults in the build do not work for you, and you need to set specific make parameters, you can save them in
Make.user. The build will automatically check for the existence of
Make.user and use it if it exists.
Building Julia requires 1.5GiB of disk space and approximately 700MiB of virtual memory.
If you need to build Julia in an environment that does not allow access to the outside world, use
make -C deps getall to download all the necessary files. Then, copy the julia directory over to the target environment and build with
Note: the build process will not work if any of the build directory's parent directories have spaces in their names (this is due to a limitation in GNU make).
Once it is built, you can run the
julia executable using its full path in the directory created above (the
julia directory), or, to run it from anywhere, either
add a soft link to the
juliaexecutable in the
/usr/local/bin(or any suitable directory already in your path), or
juliadirectory to your executable path for this shell session (in bash:
export PATH="$(pwd):$PATH"; in csh or tcsh:
set path= ( $path $cwd )), or
juliadirectory to your executable path permanently (e.g. in
Make.userand then run
Now you should be able to run Julia like this:
If everything works correctly, you will see a Julia banner and an interactive prompt into which you can enter expressions for evaluation. (Errors related to libraries might be caused by old, incompatible libraries sitting around in your PATH. In that case, try moving the
julia directory earlier in the PATH).
Your first test of Julia should be to determine whether your
build is working properly. From the UNIX/Windows command prompt inside
the julia source directory, type
make testall. You should see output
that lists a series of tests being run; if they complete without
error, you should be in good shape to start using Julia.
You can read about getting started in the manual.
If you are building a Julia package for distribution on Linux, OS X, or Windows, take a look at the detailed notes in DISTRIBUTING.md.
Julia does not install anything outside the directory it was cloned into. Julia can be completely uninstalled by deleting this directory. Julia packages are installed in
~/.julia by default, and can be uninstalled by deleting
Platform-Specific Build Notes
- GCC version 4.6 or later is recommended to build Julia.
- To use external shared libraries not in the system library search path, set
- Instead of setting
LDFLAGS, putting the library directory into the environment variable
LD_LIBRARY_PATH(at both compile and run time) also works.
- Instead of setting
- See also the external dependencies.
Julia can be built for a non-generic architecture by configuring the
ARCH Makefile variable. See the appropriate section of
Make.inc for additional customization options, such as
For example, to build for i486, set
ARCH=i486 and install the necessary system libraries for linking. On Ubuntu, these may include lib32gfortran3 (also manually call
ln -s /usr/lib32/libgfortran3.so.0 /usr/lib32/libgfortran3.so) and lib32gcc1, lib32stdc++6, among others.
You can also set
MARCH=native for a maximum-performance build customized for the current machine CPU.
The julia-deps PPA contains updated packages for julia dependencies if you want to use system libraries instead of having them downloaded and built during the build process. See System Provided Libraries.
For a fast and easy current installation, the
before_install section of travis.yml is a great resource. Note that those instructions are for Ubuntu 12.04, and for later versions you may need to install newer versions of dependencies, such as
libunwind8-dev instead of
On RHEL/CentOS 5 systems, the default compiler (
gcc 4.1) is too old to build Julia.
gfortran44 packages are installed, you can specify their use by adding the following to Make.user
FC = gfortran44 CC = gcc44 CXX = g++44
Otherwise, install or contact your systems administrator to install a more recent version of
Google Compute Engine
Google Compute Engine is evolving rapidly, as is Julia. This section is current as of March 2014 and assumes working knowledge of Google Cloud Services.
These notes apply to the Debian 7 image currently available on Google Compute Engine and Julia pre-0.3. There are only two things you need to do:
Install packages required to build on your instance:
apt-get install bzip2 gcc gfortran git g++ make m4 ncurses-dev
JuliaLang:master; you should be able to build using the generic Linux instructions. These instructions were tested on a
g1-smallinstance on 2014-03-28. Other resources include information on Google Compute Engine and a series of tutorials by Julia Ferraioli.
Linux Build Troubleshooting
|OpenBLAS build failure||Set one of the following build options in
|Illegal Instruction error||Check if your CPU supports AVX while your OS does not (e.g. through virtualization, as described in this issue), and try installing LLVM 3.3 instead of LLVM 3.2.|
It is essential to use a 64-bit gfortran to compile Julia dependencies. The gfortran-4.7 (and newer) compilers in brew and MacPorts work for building Julia.
Clang is now used by default to build Julia on OS X (10.7 and above). It is recommended that you upgrade to the latest version of Xcode (at least 4.3.3.). You need to have the Xcode command line utilities installed (and updated): run
xcode-select --install in the terminal (in Xcode prior to v5.0, you can alternatively go to Preferences -> Downloads and select the Command Line Utilities). This will ensure that clang v3.1 is installed, which is the minimum version of
clang required to build Julia. On OS X 10.6, the Julia build will automatically use
If you have set
DYLD_LIBRARY_PATH in your
.bashrc or equivalent, Julia may be unable to find various libraries that come bundled with it. These environment variables need to be unset for Julia to work.
If you see build failures in OpenBLAS or if you prefer to experiment, you can use the Apple provided BLAS in vecLib by building with
USE_SYSTEM_BLAS=1. Julia does not use the Apple provided LAPACK, as it is too old.
On FreeBSD Release 9.0, install the
gmake packages/ports, and compile Julia with the command:
$ gmake FC=gfortran46
You must use the
gmake command on FreeBSD instead of
In order to build Julia on Windows, see README.windows.
Julia can be developed in an isolated Vagrant environment. See the Vagrant README for details.
Required Build Tools and External Libraries
Building Julia requires that the following software be installed:
- GNU make — building dependencies.
- gcc & g++ or Clang — compiling and linking C, C++ (if clang, need at least v3.1, Xcode 4.3.3 on OS X)
- gfortran — compiling and linking fortran libraries
- git — version control and package management.
- perl — preprocessing of header files of libraries.
- wget, curl, or fetch (FreeBSD) — to automatically download external libraries.
- m4 — needed to build GMP.
- patch — for modifying source code.
Julia uses the following external libraries, which are automatically downloaded (or in a few cases, included in the Julia source repository) and then compiled from source the first time you run
- LLVM — compiler infrastructure.
- FemtoLisp — packaged with Julia source, and used to implement the compiler front-end.
- libuv — portable, high-performance event-based I/O library
- OpenLibm — a portable libm library containing elementary math functions.
- OpenSpecFun — a library containing Bessel and error functions of complex arguments.
- DSFMT — a fast Mersenne Twister pseudorandom number generator library.
- OpenBLAS — a fast, open, and maintained basic linear algebra subprograms (BLAS) library, based on Kazushige Goto's famous GotoBLAS. The system provided BLAS and LAPACK are used on OS X.
- LAPACK — a library of linear algebra routines for solving systems of simultaneous linear equations, least-squares solutions of linear systems of equations, eigenvalue problems, and singular value problems.
- MKL (optional) – OpenBLAS and LAPACK may be replaced by Intel's MKL library.
- AMOS — subroutines for computing Bessel and Airy functions.
- SuiteSparse — a library of linear algebra routines for sparse matrices.
- ARPACK — a collection of subroutines designed to solve large, sparse eigenvalue problems.
- FFTW — library for computing fast Fourier transforms very quickly and efficiently.
- PCRE — Perl-compatible regular expressions library.
- GMP — the GNU multiple precision arithmetic library, needed for bigint support.
- MPFR — the GNU multiple precision floating point library, needed for arbitrary precision floating point support.
- double-conversion — efficient number-to-text conversion.
System Provided Libraries
If you already have one or more of these packages installed on your system, you can prevent Julia from compiling duplicates of these libraries by passing
make or adding the line to
Make.user. The complete list of possible flags can be found in
Please be aware that this procedure is not officially supported, as it introduces additional variability into the installation and versioning of the dependencies, and is recommended only for system package maintainers. Unexpected compile errors may result, as the build system will do no further checking to ensure the proper packages are installed.
SuiteSparse is a special case, since it is typically only installed as a static library, while
USE_SYSTEM_SUITESPARSE=1 requires that it is a shared library. Running the script
contrib/repackage_system_suitesparse4.make will copy your static system SuiteSparse installation into the shared library format required by Julia.
make USE_SYSTEM_SUITESPARSE=1 will then use the SuiteSparse that has been copied into Julia's directory, but will not build a new SuiteSparse library from scratch.
Intel compilers and Math Kernel Libraries
source /path/to/mkl/bin/mklvars.sh intel64 ilp64 export MKL_INTERFACE_LAYER=ILP64
Julia can be built with the Intel compilers and MKL using the following flags.
USEICC = 1 USEIFC = 1 USE_INTEL_MKL = 1 USE_INTEL_MKL_FFT = 1 USE_INTEL_LIBM = 1
It is highly recommended to use a fresh clone of the Julia repository. Also, it is recommended that Intel compilers be used to build julia when using MKL.
Source Code Organization
The Julia source code is organized as follows:
base/ source code for Julia's standard library contrib/ editor support for Julia source, miscellaneous scripts deps/ external dependencies doc/manual source for the user manual doc/stdlib source for standard library function help text examples/ example Julia programs src/ source for Julia language core test/ test suites test/perf benchmark suites ui/ source for various front ends usr/ binaries and shared libraries loaded by Julia's standard libraries
Because of the rapid pace of development at this point, we recommend installing the latest Julia from source, but platform-specific tarballs with pre-compiled binaries are also available for download.
You can either run the
julia executable using its full path in the directory created above, or add that directory to your executable path so that you can run the Julia program from anywhere (in the current shell session):
Now you should be able to run Julia like this:
On Windows, double-click
If everything works correctly, you will see a Julia banner and an interactive prompt into which you can enter expressions for evaluation. You can read about getting started in the manual.
The following distributions include julia, but the versions may be out of date due to rapid development:
- Arch Linux
- Debian GNU/Linux
- Gentoo Linux
- Git Package in the Science overlay
- OS X Homebrew Tap
Editor and Terminal Setup
Currently, Julia editing mode support is available for Emacs, Vim, Textmate, Sublime Text, Notepad++, and Kate, in
contrib/. There is early support for IDEs such as Lighttable, QTCreator based JuliaStudio, and Eclipse (LiClipse). A notebook interface is available through IJulia, which adds Julia support to the iPython notebook.
In the terminal, Julia makes great use of both control-key and meta-key bindings. To make the meta-key bindings more accessible, many terminal emulator programs (e.g.,
xterm, etc) allow you to use the alt or option key as meta. See the section in the manual on interacting with Julia for more details.