/julia

The Julia Language: A fresh approach to technical computing.

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## 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.

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
  • GNU/Linux
  • Darwin/OS X
  • FreeBSD
  • Windows

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 and you need to use the https protocol instead of the git protocol:

git config --global url."https://".insteadOf git://

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 make.

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,

  1. add a soft link to the julia executable in the julia directory to /usr/local/bin (or any suitable directory already in your path), or

  2. add the julia directory to your executable path for this shell session (in bash: export PATH="$(pwd):$PATH" ; in csh or tcsh: set path= ( $path $cwd ) ), or

  3. add the julia directory to your executable path permanently (e.g. in .bash_profile).

Now you should be able to run Julia like this:

julia

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).

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.

## Uninstalling Julia

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 ~/.julia.

 ## Platform-Specific Build Notes

Linux

General

  • GCC version 4.6 or later is recommended to build Julia.
  • To use external shared libraries not in the system library search path, set USE_SYSTEM_XXX=1 and LDFLAGS=-Wl,-rpath /path/to/dir/contains/libXXX.so in Make.user.
    • Instead of setting LDFLAGS, putting the library directory into the environment variable LD_LIBRARY_PATH (at both compile and run time) also works.
  • See also the external dependencies.

Ubuntu

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 libunwind7-dev.

RHEL/CentOS 5

On RHEL/CentOS 5 systems, the default compiler (gcc 4.1) is too old to build Julia.

If the gcc44 and 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 gcc.

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:

  1. Install packages required to build on your instance:
apt-get install bzip2 gcc gfortran git g++ make m4 ncurses-dev
  1. Now clone JuliaLang:master and edit deps/Versions.make to select OPENBLAS_VER = v0.2.9.rc2. This picks up changes to support the Sandybridge cores used by Google Compute Engine. (Alternatively, you could fall back to the Nehelem architecture via a make option, but that would entail performance penalties.)

Now you should be able to build using the generic Linux instructions. These instructions were tested on a g1-small instance on 2014-03-28. Other resources include information on Google Compute Engine and a series of tutorials by Julia Ferraioli.

Linux Build Troubleshooting

Problem Possible Solution
OpenBLAS build failure Set one of the following build options in Make.user and build again:
  • OPENBLAS_TARGET_ARCH=BARCELONA (AMD CPUs) or OPENBLAS_TARGET_ARCH=NEHALEM (Intel CPUs)
      Set OPENBLAS_DYNAMIC_ARCH = 0 to disable compiling multiple architectures in a single binary.
  • USE_SYSTEM_BLAS=1 uses the system provided libblas
    • Set LIBBLAS=-lopenblas and LIBBLASNAME=libopenblas to force the use of the system provided OpenBLAS when multiple BLAS versions are installed
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.

OS X

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. If you do not use brew or MacPorts, you can download and install gfortran and gcc from hpc.sf.net. The HPC gfortran requires HPC gcc to be installed to function properly. 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 gcc.

If you have set LD_LIBRARY_PATH or 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.

FreeBSD

On FreeBSD Release 9.0, install the gcc46, git, and gmake packages/ports, and compile Julia with the command:

$ gmake FC=gfortran46

You must use the gmake command on FreeBSD instead of make.

Windows

In order to build Julia on Windows, see README.windows.

Vagrant

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 make:

  • 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 USE_SYSTEM_...=1 to make or adding the line to Make.user. The complete list of possible flags can be found in Make.inc.

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

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 Math Kernel Libraries

To use the Intel MKL BLAS and LAPACK libraries, make sure that MKL version 10.3.6 or higher is installed. For a 64-bit architecture, the MKL environment should be set up as:

source /path/to/mkl/bin/mklvars.sh intel64 ilp64
export MKL_INTERFACE_LAYER=ILP64

When building julia, pass the USE_MKL=1 option to make or add the following line to Make.user.

USE_MKL = 1

To rebuild a pre-built Julia source install with MKL support, delete the OpenBLAS, ARPACK, and SuiteSparse dependencies from deps, and run make cleanall testall.

## 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
## Binary Installation

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):

export PATH="$(pwd)/julia:$PATH"

Now you should be able to run Julia like this:

julia

On Windows, double-click julia.bat.

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:

## Editor and Terminal Setup

Currently, Julia editing mode support is available for Emacs, Vim, Textmate, Sublime Text, Notepad++, and Kate, in contrib/

Adjusting your terminal bindings is optional; everything will work fine without these key bindings. For the best interactive session experience, however, make sure that your terminal emulator (Terminal, iTerm, xterm, etc.) sends the ^H sequence for Backspace (delete key) and that the Shift-Enter key combination sends a \n newline character to distinguish it from just pressing Enter, which sends a \r carriage return character. These bindings allow custom readline handlers to trap and correctly deal with these key sequences; other programs will continue to behave normally with these bindings. The first binding makes backspacing through text at the prompt behave more intuitively. The second binding allows Shift-Enter to insert a newline without evaluating the current expression, even when the current expression is complete. (Pressing an unmodified Enter inserts a newline if the current expression is incomplete, evaluates the expression if it is complete, or shows an error if the syntax is irrecoverably invalid.)

On Linux systems, the Shift-Enter binding can be set by placing the following line in the file .xmodmaprc in your home directory:

keysym Return = Return Linefeed