I am trying to run a method which has an infinite loop in it to create a video display. This method is called within another loop that handles hardware input and as such cannot loop as fast as the video, causing lag if I use the outer loop to run the video. Is there a way to start the video loop and then start the hardware loop and run them separately? Currently if I call the video loop it just sits at that loop until it returns.
2 Answers
Yes, you can use Python's own threading module, or a cooperative microthreading module like gevent.
Note that Python's threading mechanism carries this disclaimer for CPython (the default Python implementation on most boxes):
Due to the Global Interpreter Lock, in CPython only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better of use of the computational resources of multi-core machines, you are advised to use
multiprocessing. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.
Depending on how the underlying modules you are calling operate, you may find that while using threading, one thread won't give up control very often, if at all. In that case, using cooperative microthreading might be your only option.
1 Comment
Jakob Bowyer
Sorry I had to rip off your first line :D
Yes, you can use Python's own multiprocessing module.
Note that Multiprocessing does not have to fight the GIL and can work simultaneously for everything you give it to do.
On the other hand there is a warning with the multiprocessing module, when you spawn a process it is a completely separate python interpreter. So its not just a OS controlled thread. It is in itself an entirely different process. This can add overhead to programs but the advantage of completely dodging the GIL makes this only a mild issue.
1 Comment
cdhowie
Yup, this can work too. It's worth noting however that the communication mechanisms you have to use become cross-process instead of cross-thread and are therefore not as simple as sharing objects, since each process gets its own heap.