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Installation#

Julia modules exist on NREL HPC systems. Access simply with

module load julia

To see all available Julia modules on the system, use the command

module spider julia

However, if you need a version of Julia for which a module does not exist or want your own personal Julia build, there are several options described in the rest of this document. Below is a general guide for what approach to use:

  • fast and easy - Anaconda
  • performance and ease - Spack
  • performance or need to customize Julia build - do it yourself (i.e. build from source)

Anaconda#

Older versions of Julia are available from conda-forge channel

conda create -n julia-env
source activate julia-env
conda install -c conda-forge julia

Spack Build#

Prerequisites#

A working version of Spack. For detailed instructions on getting Spack setup see the GitHub repository. Briefly, this can be done with the following

git clone https://github.com/spack/spack.git
cd spack
git checkout releases/v0.15 # Change to desired release
. share/spack/setup-env.sh # Activate spack shell support

Instructions#

Info

Steps 1 and 2 may be skipped when using the develop branch or any release branch after v0.15.

  1. In the Spack repository, open the file var/spack/repos/builtin/packages/julia/package.py in your favorite editor.
  2. There is an if-else statement under the if statement 
    if spec.target.family == 'x86_64'  or spec.target.family == 'x86':
    Change the else clause to read 
    else:
    target_str = str(spec.target).replace('_','-')
    options += [
        'MARCH={0}'.format(target_str),
        'JULIA_CPU_TARGET={0}'.format(target_str)
    ]
  3. Now install Julia with Spack 
    spack install julia

Do It Yourself Build (v 1.2 or later)#

Prerequisites#

All the required build tools and libraries are available on the clusters either by default or through modules. The needed modules are covered in the instructions.

Terms#

  • JULIA_HOME is the base directory of Julia source code (initially called julia after git clone)

Instructions#

When compiling Julia you can choose to compile against Intel's MKL libraries or OpenBLAS for the Julia linear algebra operations. If you are going to be doing significant matrix-vector operations directly in Julia, then you will want to compile it with MKL. If most of the matrix-vector operations are being done in a subprogram or library (e.g. Ipopt) then it will make no difference what you compile Julia with. In this latter case, it is recommended that you compile with OpenBLAS since that is significantly easier. Instructions for both choices are given below.

Note

When compiling Julia with MKL, Julia uses the single dynamic library option for linking. Any dynamic libraries (e.g. Ipopt or CoinHSL) loaded by Julia also need to be linked to MKL with this approach. Failing to do so will result in unusual behavior, e.g. getting garbage values passed to the MKL function calls.

Info

We are using Eagle as an example for this demonstration. The process remains the same for other clusters.

  1. Load the following modules:
    • gcc (>= 5.1)
    • cmake (>= 3.4.3)
    • mkl (any version -- optional)
  2. Get the Julia source code git clone https://github.com/JuliaLang/julia.git
  3. cd julia
  4. Change to the version of Julia you want to build git checkout <julia_version>
  5. In Make.user (you will need to create the file if it doesn't exist) in JULIA_HOME put the following:
    • MARCH=skylake-avx512 -- tell the compiler to optimize floating point instructions for Eagle's Skylake processors
    • If you want to compile Julia with MKL also add the following
      • USE_INTEL_MKL=1 -- Use Intel versions of BLAS and LAPACK (this is why we loaded mkl module)
      • USE_BLAS64=0 -- Use the 64-bit library with the 32-bit integer interface. This will necessitate changes in Make.inc. The reasons for this are discussed in step 7.

    Tip

    I found it useful to create the file Make.user in another location (e.g. home directory) and drop a link into the Julia build directory as I used git clean -x -f -d to make sure everything is completely clean

  6. (Skip to step 8 if compiling Julia without MKL.) There are a couple of problems to overcome when compiling Julia with MKL. The first is that a makefile in the SuiteSparse library package defines a USER variable that leads to problems with xalt/ld (a script that invokes ld). To fix this do the following:
    • In JULIA_HOME fetch and unpack the SuiteSparse libraries make -C deps/ extract-suitesparse
    • With your favorite editor, open the file JULIA_HOME/deps/scratch/SuiteSparse-5.4.0/UMFPACK/Lib/Makefile
    • In the Makefile, do a global replace on USER --i.e. change all occurrences of the variable USER to something else like MUSER
  7. The second problem is that when compiling against MKL, Julia either uses the 32-bit MKL libraries or the 64-bit MKL libraries with 64-bit interface. It is common for other libraries (e.g. Ipopt or HSL) to compile against the 64-bit MKL libraries with 32-bit interface. This causes unusual behavior. To make Julia compile against the 64-bit MKL libraries with 32-bit interface, do the following:
    • Open Make.inc in your favorite editor and make the following change
      • find where MKLLIB is set (there will be an if-else statement depending on the value of USE_BLAS64)
      • change the else clause to read MKLLIB := $(MKLROOT)/lib/intel64
  8. make -j4 -- -j4 allows make to use 4 processes to build and can speed up compilation (additional speed ups may be possible by increasing the number of processes)