["python","multithreading","performance"]
Python threads all executing on a single core

I have a Python program that spawns many threads, runs 4 at a time, and each performs an expensive operation. Pseudocode: for object in list: t = Thread(target=process, args=(object)) # if fewer than 4 threads are currently running, t.start(). Otherwise, add t to queue But when the program is run, Activity Monitor in OS X shows that 1 of the 4 logical cores is at 100% and the others are at nearly 0. Obviously I can't force the OS to do anything but I've never had to pay attention to performance in multi-threaded code like this before so I was wondering if I'm just missing or misunderstanding something. Thanks.
Note that in many cases (and virtually all cases where your "expensive operation" is a calculation implemented in Python), multiple threads will not actually run concurrently due to Python's Global Interpreter Lock (GIL). The GIL is an interpreter-level lock. This lock prevents execution of multiple threads at once in the Python interpreter. Each thread that wants to run must wait for the GIL to be released by the other thread, which means your multi-threaded Python application is essentially single threaded, right? Yes. Not exactly. Sort of. CPython uses what’s called “operating system” threads under the covers, which is to say each time a request to make a new thread is made, the interpreter actually calls into the operating system’s libraries and kernel to generate a new thread. This is the same as Java, for example. So in memory you really do have multiple threads and normally the operating system controls which thread is scheduled to run. On a multiple processor machine, this means you could have many threads spread across multiple processors, all happily chugging away doing work. However, while CPython does use operating system threads (in theory allowing multiple threads to execute within the interpreter simultaneously), the interpreter also forces the GIL to be acquired by a thread before it can access the interpreter and stack and can modify Python objects in memory all willy-nilly. The latter point is why the GIL exists: The GIL prevents simultaneous access to Python objects by multiple threads. But this does not save you (as illustrated by the Bank example) from being a lock-sensitive creature; you don’t get a free ride. The GIL is there to protect the interpreters memory, not your sanity. See the Global Interpreter Lock section of Jesse Noller's post for more details. To get around this problem, check out Python's multiprocessing module. multiple processes (with judicious use of IPC) are[...] a much better approach to writing apps for multi-CPU boxes than threads. -- Guido van Rossum (creator of Python) Edit based on a comment from @spinkus: If Python can't run multiple threads simultaneously, then why have threading at all? Threads can still be very useful in Python when doing simultaneous operations that do not need to modify the interpreter's state. This includes many (most?) long-running function calls that are not in-Python calculations, such as I/O (file access or network requests)) and [calculations on Numpy arrays][6]. These operations release the GIL while waiting for a result, allowing the program to continue executing. Then, once the result is received, the thread must re-acquire the GIL in order to use that result in "Python-land"