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Julia on Raspberry Pi - a boost on Computation

Julia on Raspberry Pi - a boost on Computation

 Largely, C and C++ became centric for propitiatory language for accessing the machine code, hence most languages build with completion to overdo certain patches for compiler, assembler, and translator support.

But, Julia, on the other hand, has been developed with making out of machine code to interact powerful computation with hardware.

Over the years, Computers have been spoken for software but the reality is never boomed to see the features of hardware potential.

Logics are harder as the mounting languages on the developing stage but with time, we can play any sport better, just like Julia evolved over the years.

Generally, tech giants won't like these languages because they plan too much in the future. For example, Windows wants MatLab to be bigger as it occupies users for them. Actually, we can pinpoint like Python now being largely celebrated because it does most of MatLab operations.

But, as a Mechatronics Engineer, I personally know the python is left back with more void in performance to the computing.

Julia is initially a language for the dynamics of Mathematics, yes for working with equations like Differential calculus, Integral Calculus, and more.

Julia computations are very much similar to a compiler interacting directly with kernel modules and hardware. Hence speed is an obvious thing.

In General scope, MatLab too has these functional programming modules of mathematical modeling of a live system into the computation.

Julia just made them open-source now, with every new technique in the Computation industry is more abstract from Mechatronics Engineering is all fun from my perspective.

Julia on Edge will boost performance on the scale of the processor and hardware.

On, Raspberry Pi 4 - Julia makes a sounding replacement for C++ as the machine learning and deep learning support modules programming in C++ is really a hard thing for beginners.

Julia on syntax is more likely python but can do much bigger things. 

Since Raspberry Pi is a data computing module, Julia is building for data.

It offers several features,

  • Data Visualization and Plotting
  • Scalable Machine Learning
  • Rich Ecosystem for Scientific Computing
  • Parallel and Heterogeneous Computing

Installing Julia (Recommended)

The easiest way to install Julia is by downloading the 32-bit (ARMv7-a hard float) prebuilt binary from the JuliaLang website.

An older version of Julia (1.0.3) is also available via in Raspbian, we hope to update this to the latest version in the near future. (This is the easiest way to install Julia and it adds Julia to PATH automatically.)

sudo apt install julia

Please read below if you would like to compile Julia instead of installing Julia using the methods above.

Compiling Julia

If you have installed Julia following the instructions above, there is no need to compile Julia. This method takes a very long time and takes up a lot of storage.

For those who are interested in compiling Julia, instructions can be found over here

IJulia notebook

This is optional, and is only for those who need Jupyter Notebook.

Jupyter will need to be installed manually, as the automatic Conda installer does not work on the ARM architecture. Generally, running

sudo apt install libzmq3-dev
sudo pip3 install jupyter

at the shell should work. Then it should be sufficient to do


at the Julia REPL.


The JuliaBerry org provides several Raspberry Pi-specific packages:

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