Task-based parallel programming model in Python. Run complex workflows on large computer clusters or parallelize codes on your laptop: Noodles offers the same intuitive interface.

539 commits | Last update: May 15, 2018

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What Noodles can do for you

  • Enables scientists to execute and restart parallel workflows by readable and easily maintainable Python code
  • Helps to scale computations in Python with complex dependencies to a parallel environment
  • No need to leave the comfort of Python: Noodles is a thin layer, unobtrusively handling complexity of a concurrent environment.

Often, a computer program can be sped up by executing parts of its code in parallel (simultaneously), as opposed to synchronously (one part after another).

A simple example may be where you assign two variables, as follows

a = 2 * i


b = 3 * i.

Either statement is only dependent on i, but whether you assign a before b or vice versa, does not matter for how your program works. Whenever this is the case, there is potential to speed up a program, because the assignment of a and b could be done in parallel, using multiple cores on your computer's CPU. Obviously, for simple assignments like

a = 2 * i,

there is not much time to be gained, but what if a is the result of a time-consuming function, e.g.

a = very_difficult_function(i)?

And what if your program makes many calls to that function, e.g.

list_of_a = [very_difficult_function(i) for i in list_of_i]?

The potential speed-up could be tremendous.

So, parallel execution of computer programs is great for improving performance, but how do you tell the computer which parts should be executed in parallel, and which parts should be executed synchronously? How do you identify the order in which to execute each part, since the optimal order may be different from the order in which the parts appear in your program. These questions quickly become nearly impossible to answer as your program grows and changes during development. Because of this, many developers accept the slow execution of their program only because it saves them from the headaches associated with keeping track of which parts of their program depend on which other parts.

Enter Noodles.

Noodles is a Python package that can automatically construct a callgraph for a given Python program, listing exactly which parts depend on which parts. Moreover, Noodles can subsequently use the callgraph to execute code in parallel on your local machine using multiple cores. If you so choose, you can even configure Noodles such that it will execute the code remotely, for example on a big compute node in a cluster computer.

Read more
  • Workflow technologies
  • High performance computing
Programming Language
  • Python
  • Apache-2.0

Participating organizations


  • Johannes Hidding
    Netherlands eScience Center
  • Berend Weel
    Netherlands eScience Center
  • Vincent van Hees
    Netherlands eScience Center
  • Felipe Zapata
    Netherlands eScience Center
  • Hanno Spreeuw
    Netherlands eScience Center
  • Joris Borgdorff
    Netherlands eScience Center
  • Lars Ridder
    Netherlands eScience Center
  • Ben van Werkhoven
    Netherlands eScience Center
  • Arnold Kuzniar
    Netherlands eScience Center
Show all contributors
Contact person
Johannes Hidding
Netherlands eScience Center