SCOOP (Scalable COncurrent Operations in Python) is a distributed task module allowing concurrent parallel programming on various environments, from heterogeneous grids to supercomputers.


Our philosophy is based on these ideas:

  • The future is parallel;
  • Simple is beautiful;
  • Parallelism should be simpler.

These tenets are translated concretely in a minimum number of functions allowing maximum parallel efficiency while keeping at minimum the inner knowledge required to use them. It is implemented with Python 3 in mind while being compatible with 2.6+ to allow fast prototyping without sacrificing efficiency and speed.


SCOOP has many features and advantages over Futures, multiprocessing and similar modules, such as:

  • Harness the power of multiple computers over network;
  • Ability to spawn subtasks within tasks;
  • API compatible with PEP 3148;
  • Parallelizing serial programs with only minor modifications;
  • Efficient load-balancing.

Anatomy of a SCOOPed program

SCOOP can handle multiple diversified multi-layered tasks. You can submit your different functions and data simultaneously and effortlessly while the framework executes them locally or remotely. Contrarily to most multiprocessing frameworks, it allows to launch subtasks within tasks.


Through SCOOP, you can simultaneously execute tasks that are of different nature (Discs of different colors) or different by complexity (Discs radiuses). The module will handle the physical considerations of parallelization such as task distribution over your resources (load balancing), communications, etc.


The common applications of SCOOP consist of, but is not limited to:

  • Evolutionary Algorithms
  • Monte Carlo simulations
  • Data mining
  • Data processing
  • I/O processing
  • Graph traversal

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