Python's multi-threaded code is slower than single-threaded

Question

I've first observed this issue in a production code, then made a prototype:

import threading, Queue, time, sys

def heavyfunc():
    ''' The idea is just to load CPU '''
    sm = 0
    for i in range(5000):
        for j in range(5000):
            if i + j % 2 == 0:
                sm += i - j
    print "sm = %d" % sm

def worker(queue):
    ''' worker thread '''
    while True:
        elem = queue.get()
        if elem == None: break
        heavyfunc()           # whatever the elem is


starttime = time.time()  

q = Queue.Queue()             # queue with tasks

number_of_threads = 1
# create & start number_of_threads working threads
threads = [threading.Thread(target=worker, args=[q]) for thread_idx in range(number_of_threads)]
for t in threads: t.start()

# add 2 working items: they are estimated to be computed in parallel
for x in range(2):
    q.put(1)

for t in threads: q.put(None) # Add 2 'None' => each worker will exit when gets them
for t in threads: t.join()    # Wait for every worker

#heavyfunc()

elapsed = time.time() - starttime

print >> sys.stderr, elapsed

The idea of heavyfunc() is just to load CPU, without any synchronization and dependencies.

When using 1 thread, it takes 4.14 sec in average When using 2 threads, it takes 6.40 sec in average When not using any threads, to compute heavyfunc() takes 2.07 sec in average (measured many times, that's exactly 4.14 / 2, as in case with 1 thread and 2 tasks).

I'm expecting 2 jobs with heavyfunc() to take 2.07 sec, provided there are 2 threads. (My CPU is i7 => there are enough cores).

Here is the CPU monitor's screenshots that also give the idea there were no true multithreading:

CPU Load Graphi

Where is the error in my thinking? How do I create n threads that don't interfere?

As the pretty large note at the start of the documentation of the threading module says.. only one thread in CPython, use processes. — Voo
This is well-known Python behaviour, see Understandind the Python GIL. — Sven Marnach
@Voo: your restatement of the restriction isn't quite right. You can have many threads, but only one can execute Python bytecode or manipulate Python objects at a time. — Ned Batchelder
@Ned Yes quite the informal description, still I'm sure nobody would misunderstand it, especially since I refer to the actual documentation that mentions the GIL, yada yada in detail. It's a comment after all, no whole answer. — Voo

Ned Batchelder Ned Batchelder · Accepted Answer · 2012-06-28T15:56:10

CPython will not execute bytecode on more than one core at once. Multi-threading cpu-bound code is pointless. The Global Interpreter Lock (GIL) is there to protect all of the reference counts in the process, so only one thread can use Python objects at a time.

You are seeing worse performance because you still only have one thread at a time working, but now you are also changing thread contexts.

Python's multi-threaded code is slower than single-threaded

1 Answers