tasks | Parallelize network dependent tasks in python | Job Scheduling library

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I'm trying to parallelize a merge-sort algorithm. What I'm doing is dividing the input array for each thread, then merging the threads results.

Ok, but yours is an unnecessarily difficult approach. At each step of the merge process, you want half of your threads to wait for the other half to finish, and the most natural way for one thread to wait for another to finish is to use pthread_join(). If you wanted all of your threads to continue with more work after synchronizing then that would be different, but in this case, those that are not responsible for any more merges have nothing at all left to do.

This is what I've tried:

Update:

I added the following code to refresh the core data before I fetch. Everything work as expect.

Send your data to separate processes. I used concurrent.futures because I like the simple interface.

This runs in about 11 seconds on my computer.

We're looking in to the same issue currently and it seems apple are disabling the old binary interface which push sharp uses.

https://developer.apple.com/news/?id=c88acm2b

pushsharp has it on the roadmap to support the new interface but not completed yet.

Found this library which seems easy enough to use as a solution. hope this helps.

https://github.com/alexalok/dotAPNS

This is exactly the reason why coroutines were invented and how they differ from threaded concurrency. Coroutines don't block, but suspend (well, they can do both). And "suspend" isn't just another name for "block". When they suspend (e.g. by invoking join()), they effectively free the thread that runs them, so it can do something else somewhere else. And yes, it sounds like something that is technically impossible, because we are in the middle of executing the code of some function and we have to wait there, but well... welcome to coroutines :-)

You can think of it as the function is being cut into two parts: before join() and after it. First part schedules the background operation and immediately returns. When background operation finishes, it schedules the second part on the main thread. This is not how coroutines works internally (functions aren't really cut, they create continuations), but this is how you can easily imagine them working if you are familiar with executors or event loops.

delay() is also a suspending function, so it frees the thread running it and schedules execution of the code below it after a specified duration.

You need to code the branch of your workflow tree in separate statements.

Pythons multiprocessing package is limited to shared memory parallelization. It spawns new processes that all have access to the main memory of a single machine.

You cannot simply scale out such a software onto multiple nodes. As the different machines do not have a shared memory that they can access.

To run your program on multiple nodes at once, you should have a look into MPI (Message Passing Interface). There is also a python package for that.

Depending on your task, it may also be suitable to run the program 4 times (so one job per node) and have it work on a subset of the data. It is often the simpler approach, but not always possible.