Programming Languages on Power

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Explore the benefits of using Julia on IBM Power then give it a try

By Swati Karmarkar posted Wed September 07, 2022 09:57 AM


Julia is a general-purpose programming language with a special focus on scientific computing. Julia is as fast as C programming language in many cases while remaining dynamic like Python.

We are happy to announce that as of Dec 19, 2021, Julia 1.6.2 is supported on the IBM Power platform, "Tier-3" level. For more information about Tier 3, refer to the Julia download page.

The popularity of Julia has been soaring since it first became available in 2012, and it is currently in the top 30 programming languages as per the TIOBE index.

Refer to the following interesting articles about Julia:

Benefits of Julia on IBM Power

Designed with machine learning (ML) in mind, Julia focuses on the scientific computing domain and its need for parallel, distributed, and intensive computation tasks. Julia has a powerful suite of libraries for developing artificial intelligence which includes general linear models, decision trees, clustering, Flux for deep learning, text analysis for natural language processing, and so on. Julia benefits to Power in the following ways:

  • Julia is an easy programming language to learn. Its syntax is similar to Python and MATLAB, and can be easily adapted on Power. The following code snippet shows the similarity among Julia, MATLAB, and Python languages:

    Description MATLAB syntax Python syntax Julia syntax
    Import packages Nil import numpy as np
    import matpotlib.pyplot as plt
    Import Pkg
    using Plots
    Define the variable for X axis x = 0 : pi/10 : 2pi; x=np.linespace(0, 2np.pi, 21) x = 0 : pi/10 : 2*pi;
    Define the variable for Y axis y = sin(x); y = np.sin(x) y = sin.(x);
    Code snippet to plot the graph plot(x,y)
    title(‘My first plot’)
    plt.title(‘My first plot’)
    title=”My first plot”,

  • Julia integrates well with the existing code and platforms such as IBM Power.

  • Julia combines the familiarity and ease of use of Python and R with the speed and the strength of C, C++, or Java. So programmers no longer need to estimate models in one language and reproduce them in a faster production language.

Check out Why Data Scientists Are Falling in Love With Julia.

What did we do and how did it work?

Try Julia on Power

Perform the following steps to install and run Julia on a Power virtual machine (VM):
  1. Download the latest stable build of Julia. Enter the following command on the Power system to get the TAR file for Power (ppc64le):
  2. Extract the .tar file.
    tar -xvzf julia-1.6.2-linux-ppc64le.tar.gz
  3. Enter the following commands to run the Julia binary file present in the bin/julia directory of the extracted directory:
    [user1@p006vm71 ~]$ cd julia-1.6.2
    [user1@p006vm71 julia-1.6.2]$ bin/julia

    [user1@p006vm71 julia-1.6.2]$ export PATH=$PATH:~/julia-1.6.2/bin
    [user1@p006vm71 julia-1.6.2]$ julia

The command prompt of the Julia console looks as follows:


How can you use Julia?

Julia can be used to run scripts and many other commands that can provide simple to complex output. Following are some of the examples:

  • Use the following Julia command to find the platform and version information:

    julia> versioninfo()
    Julia Version 1.8.0-DEV.889
    Commit f14e44f38b* (2021-11-03 05:54 UTC)
    Platform Info:
    OS: Linux (ppc64le-redhat-linux)
    CPU: POWER8 (architected), altivec supported
    WORD_SIZE: 64
    LIBM: libopenlibm
    LLVM: libLLVM-12.0.1 (ORCJIT, pwr8)​
  • Run the following Julia code snippet to find the CPU information:

    julia> ccall(:jl_dump_host_cpu, Cvoid, ())
    CPU: pwr8

  • Run a .jl file to get the required output. Following are the steps to run a sample .jl file:

    1. Write the following code in Julia (that plots a graph on the X and Y axis) and save it as plots.jl:
      import Pkg
      using Plots
      # plot some data
      plot([cumsum(rand(500) .- 0.5), cumsum(rand(500) .- 0.5)])
      # save the current figure
    2. Run plots.jl as:
      julia plots.jl

The output is as follows:


For more information about working with Julia, visit


The author would like to thank her co-author, Amit Sadaphule and Valentin Churavy, Nemanja Klimnovic, and Hari Reddy for helping to get Julia running on Power.

Reference articles