Is python a serious option for concurrent programming

just considering starting to learning python but I have one concern before I invest more time. Let me phrase this as a statement followed by a concern for others to comment on as perhaps the assumptions in the statement are inv开发者_Go百科alid:

I have read about GIL and the consensus seems to be if you require concurrent solutions in python your best bet is to fork a new process to avoid GIL.

My concern is that if I have a problem I'd like to split into N*2 pieces across N processors (assume for example I have a single server running a *nix o/s with say 8 cores) I will incur context switching penalties between processes rather than between threads, which is more costly, which will limit performance.

I ask this because other languages are out there that claim to excel in such a scenario and I wonder is python appropriate for this arena.

multiprocessing can get around the GIL, but it introduces its own issues such as communication between the processes.

Python is not very good for CPU-bound concurrent programming. The GIL will (in many cases) make your program run as if it was running on a single core - or even worse. Even Unladen Swallow will (probably) not solve that problem (quote from their project plan: "we are no longer as optimistic about our chances of removing the GIL completely").

As you already stated, other languages claim to be better in concurrent programming. Haskell, for example, has built-in functionality for programming concurrent applications. You could also try C++ with OpenMP, which I think makes parallelization very simple.

If your application is I/O-bound, Python may be a serious solution as the GIL is normally released while doing blocking calls.

In my limited experience, the "context switch cost" is overrated as a performance limitation.

I/O bandwidth and memory are the most common limiting factors. Python's I/O is comparable to many other languages, since it simply uses the standard C libraries pretty directly.

Your actual problem may not be typical. However, many problems work out really well in multi-processing mode because they're actually I/O bound. Often it's filesystem, web page reading or Database operations that limit performance long before context switches.

A very glib one-word response: Golang.

If you're considering learning Python for addressing this problem, I might suggest taking a look at Erlang instead. It has excellent support for very lightweight processes, and built-in primitives for IPC.

Not to discourage you from learning Python, of course, just suggesting there might be a better tool for this particular task.

Also if you are looking at object sharing between the python processes, i suggest you look at the answer by Alex in this question

writing codes for multiple process is not an easy task.

But if you start thinking to this way first, eventually, it is easier to scale if one machine isn't enough...Threads can't be used across machines...