lox: Concurrency Made Easy

Many programs are embaressingly parallel and can gain large performance boost by simply parallelizing portions of the code. However, multithreading a program is still typically seen as a difficult task and placed at the bottom of the TODO list. lox aims to make it as simple and intuitive as possible to parallelize functions and methods in python. This includes both invoking functions, as well as providing easy-to-use guards for shared resources.

lox provides a simple, shallow learning-curve toolset to implement multithreading or multiprocessing that will work in most projects. lox is not meant to be the bleeding edge of performance; for absolute maximum performance, you code will have to be more fine tuned and may benefit from python3’s builtin asyncio, greenlet, or other async libraries. lox’s primary goal is to provide that maximum concurrency performance in the least amount of time and the smallest refactor.

A very simple example is as follows.

>>> import lox
>>> @lox.thread(4) # Will operate with a maximum of 4 threads
... def foo(x,y):
...     return x*y
>>> foo(3,4)
>>> for i in range(5):
...     foo.scatter(i, i+1)
>>> # foo is currently being executed in 4 threads
>>> results = foo.gather() # block until results are ready
>>> print(results) # Results are in the same order as scatter() calls
[0, 2, 6, 12, 20]


  • Multithreading: Powerful, intuitive multithreading in just 2 additional lines of code.
  • Multiprocessing: Truly parallel function execution with the same interface as multithreading.
  • Synchronization: Advanced thread synchronization, communication, and resource management tools.

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