What are the differences between the threading and multiprocessing modules?
Question:
I am learning how to use the threading and the multiprocessing modules in Python to run certain operations in parallel and speed up my code.
I am finding this hard (maybe because I don’t have any theoretical background about it) to understand what the difference is between a threading.Thread() object and a multiprocessing.Process() one.
Also, it is not entirely clear to me how to instantiate a queue of jobs and having only 4 (for example) of them running in parallel, while the other wait for resources to free before being executed.
I find the examples in the documentation clear, but not very exhaustive; as soon as I try to complicate things a bit, I receive a lot of weird errors (like a method that can’t be pickled, and so on).
So, when should I use the threading and multiprocessing modules?
Can you link me to some resources that explain the concepts behind these two modules and how to use them properly for complex tasks?
Answers:
Multiple threads can exist in a single process.
The threads that belong to the same process share the same memory area (can read from and write to the very same variables, and can interfere with one another).
On the contrary, different processes live in different memory areas, and each of them has its own variables. In order to communicate, processes have to use other channels (files, pipes or sockets).
If you want to parallelize a computation, you’re probably going to need multithreading, because you probably want the threads to cooperate on the same memory.
Speaking about performance, threads are faster to create and manage than processes (because the OS doesn’t need to allocate a whole new virtual memory area), and inter-thread communication is usually faster than inter-process communication. But threads are harder to program. Threads can interfere with one another, and can write to each other’s memory, but the way this happens is not always obvious (due to several factors, mainly instruction reordering and memory caching), and so you are going to need synchronization primitives to control access to your variables.
Well, most of the question is answered by Giulio Franco. I will further elaborate on the consumer-producer problem, which I suppose will put you on the right track for your solution to using a multithreaded app.
fill_count = Semaphore(0) # items produced
empty_count = Semaphore(BUFFER_SIZE) # remaining space
buffer = Buffer()
def producer(fill_count, empty_count, buffer):
while True:
item = produceItem()
empty_count.down();
buffer.push(item)
fill_count.up()
def consumer(fill_count, empty_count, buffer):
while True:
fill_count.down()
item = buffer.pop()
empty_count.up()
consume_item(item)
You could read more on the synchronization primitives from:
http://linux.die.net/man/7/sem_overview
http://docs.python.org/2/library/threading.html
The pseudocode is above. I suppose you should search the producer-consumer-problem to get more references.
What Giulio Franco says is true for multithreading vs. multiprocessing in general.
However, Python* has an added issue: There’s a Global Interpreter Lock that prevents two threads in the same process from running Python code at the same time. This means that if you have 8 cores, and change your code to use 8 threads, it won’t be able to use 800% CPU and run 8x faster; it’ll use the same 100% CPU and run at the same speed. (In reality, it’ll run a little slower, because there’s extra overhead from threading, even if you don’t have any shared data, but ignore that for now.)
There are exceptions to this. If your code’s heavy computation doesn’t actually happen in Python, but in some library with custom C code that does proper GIL handling, like a numpy app, you will get the expected performance benefit from threading. The same is true if the heavy computation is done by some subprocess that you run and wait on.
More importantly, there are cases where this doesn’t matter. For example, a network server spends most of its time reading packets off the network, and a GUI app spends most of its time waiting for user events. One reason to use threads in a network server or GUI app is to allow you to do long-running “background tasks” without stopping the main thread from continuing to service network packets or GUI events. And that works just fine with Python threads. (In technical terms, this means Python threads give you concurrency, even though they don’t give you core-parallelism.)
But if you’re writing a CPU-bound program in pure Python, using more threads is generally not helpful.
Using separate processes has no such problems with the GIL, because each process has its own separate GIL. Of course you still have all the same tradeoffs between threads and processes as in any other languages—it’s more difficult and more expensive to share data between processes than between threads, it can be costly to run a huge number of processes or to create and destroy them frequently, etc. But the GIL weighs heavily on the balance toward processes, in a way that isn’t true for, say, C or Java. So, you will find yourself using multiprocessing a lot more often in Python than you would in C or Java.
Meanwhile, Python’s “batteries included” philosophy brings some good news: It’s very easy to write code that can be switched back and forth between threads and processes with a one-liner change.
If you design your code in terms of self-contained “jobs” that don’t share anything with other jobs (or the main program) except input and output, you can use the concurrent.futures library to write your code around a thread pool like this:
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
executor.submit(job, argument)
executor.map(some_function, collection_of_independent_things)
# ...
You can even get the results of those jobs and pass them on to further jobs, wait for things in order of execution or in order of completion, etc.; read the section on Future objects for details.
Now, if it turns out that your program is constantly using 100% CPU, and adding more threads just makes it slower, then you’re running into the GIL problem, so you need to switch to processes. All you have to do is change that first line:
with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
The only real caveat is that your jobs’ arguments and return values have to be pickleable (and not take too much time or memory to pickle) to be usable cross-process. Usually this isn’t a problem, but sometimes it is.
But what if your jobs can’t be self-contained? If you can design your code in terms of jobs that pass messages from one to another, it’s still pretty easy. You may have to use threading.Thread or multiprocessing.Process instead of relying on pools. And you will have to create queue.Queue or multiprocessing.Queue objects explicitly. (There are plenty of other options—pipes, sockets, files with flocks, … but the point is, you have to do something manually if the automatic magic of an Executor is insufficient.)
But what if you can’t even rely on message passing? What if you need two jobs to both mutate the same structure, and see each others’ changes? In that case, you will need to do manual synchronization (locks, semaphores, conditions, etc.) and, if you want to use processes, explicit shared-memory objects to boot. This is when multithreading (or multiprocessing) gets difficult. If you can avoid it, great; if you can’t, you will need to read more than someone can put into an SO answer.
From a comment, you wanted to know what’s different between threads and processes in Python. Really, if you read Giulio Franco’s answer and mine and all of our links, that should cover everything… but a summary would definitely be useful, so here goes:
- Threads share data by default; processes do not.
- As a consequence of (1), sending data between processes generally requires pickling and unpickling it.**
- As another consequence of (1), directly sharing data between processes generally requires putting it into low-level formats like Value, Array, and
ctypes types.
- Processes are not subject to the GIL.
- On some platforms (mainly Windows), processes are much more expensive to create and destroy.
- There are some extra restrictions on processes, some of which are different on different platforms. See Programming guidelines for details.
- The
threading module doesn’t have some of the features of the multiprocessing module. (You can use multiprocessing.dummy to get most of the missing API on top of threads, or you can use higher-level modules like concurrent.futures and not worry about it.)
* It’s not actually Python, the language, that has this issue, but CPython, the “standard” implementation of that language. Some other implementations don’t have a GIL, like Jython.
** If you’re using the fork start method for multiprocessing—which you can on most non-Windows platforms—each child process gets any resources the parent had when the child was started, which can be another way to pass data to children.
I believe this link answers your question in an elegant way.
To be short, if one of your sub-problems has to wait while another finishes, multithreading is good (in I/O heavy operations, for example); by contrast, if your sub-problems could really happen at the same time, multiprocessing is suggested. However, you won’t create more processes than your number of cores.
Here’s some performance data for python 2.6.x that calls to question the notion that threading is more performant that multiprocessing in IO-bound scenarios. These results are from a 40-processor IBM System x3650 M4 BD.
IO-Bound Processing : Process Pool performed better than Thread Pool
>>> do_work(50, 300, 'thread','fileio')
do_work function took 455.752 ms
>>> do_work(50, 300, 'process','fileio')
do_work function took 319.279 ms
CPU-Bound Processing : Process Pool performed better than Thread Pool
>>> do_work(50, 2000, 'thread','square')
do_work function took 338.309 ms
>>> do_work(50, 2000, 'process','square')
do_work function took 287.488 ms
These aren’t rigorous tests, but they tell me that multiprocessing isn’t entirely unperformant in comparison to threading.
Code used in the interactive python console for the above tests
from multiprocessing import Pool
from multiprocessing.pool import ThreadPool
import time
import sys
import os
from glob import glob
text_for_test = str(range(1,100000))
def fileio(i):
try :
os.remove(glob('./test/test-*'))
except :
pass
f=open('./test/test-'+str(i),'a')
f.write(text_for_test)
f.close()
f=open('./test/test-'+str(i),'r')
text = f.read()
f.close()
def square(i):
return i*i
def timing(f):
def wrap(*args):
time1 = time.time()
ret = f(*args)
time2 = time.time()
print '%s function took %0.3f ms' % (f.func_name, (time2-time1)*1000.0)
return ret
return wrap
result = None
@timing
def do_work(process_count, items, process_type, method) :
pool = None
if process_type == 'process' :
pool = Pool(processes=process_count)
else :
pool = ThreadPool(processes=process_count)
if method == 'square' :
multiple_results = [pool.apply_async(square,(a,)) for a in range(1,items)]
result = [res.get() for res in multiple_results]
else :
multiple_results = [pool.apply_async(fileio,(a,)) for a in range(1,items)]
result = [res.get() for res in multiple_results]
do_work(50, 300, 'thread','fileio')
do_work(50, 300, 'process','fileio')
do_work(50, 2000, 'thread','square')
do_work(50, 2000, 'process','square')
Python documentation quotes
I’ve highlighted the key Python documentation quotes about Process vs Threads and the GIL at: What is the global interpreter lock (GIL) in CPython?
Process vs thread experiments
I did a bit of benchmarking in order to show the difference more concretely.
In the benchmark, I timed CPU and IO bound work for various numbers of threads on an 8 hyperthread CPU. The work supplied per thread is always the same, such that more threads means more total work supplied.
The results were:
Conclusions:
-
for CPU bound work, multiprocessing is always faster, presumably due to the GIL
-
for IO bound work. both are exactly the same speed
-
threads only scale up to about 4x instead of the expected 8x since I’m on an 8 hyperthread machine.
Contrast that with a C POSIX CPU-bound work which reaches the expected 8x speedup: What do 'real', 'user' and 'sys' mean in the output of time(1)?
TODO: I don’t know the reason for this, there must be other Python inefficiencies coming into play.
Test code:
#!/usr/bin/env python3
import multiprocessing
import threading
import time
import sys
def cpu_func(result, niters):
'''
A useless CPU bound function.
'''
for i in range(niters):
result = (result * result * i + 2 * result * i * i + 3) % 10000000
return result
class CpuThread(threading.Thread):
def __init__(self, niters):
super().__init__()
self.niters = niters
self.result = 1
def run(self):
self.result = cpu_func(self.result, self.niters)
class CpuProcess(multiprocessing.Process):
def __init__(self, niters):
super().__init__()
self.niters = niters
self.result = 1
def run(self):
self.result = cpu_func(self.result, self.niters)
class IoThread(threading.Thread):
def __init__(self, sleep):
super().__init__()
self.sleep = sleep
self.result = self.sleep
def run(self):
time.sleep(self.sleep)
class IoProcess(multiprocessing.Process):
def __init__(self, sleep):
super().__init__()
self.sleep = sleep
self.result = self.sleep
def run(self):
time.sleep(self.sleep)
if __name__ == '__main__':
cpu_n_iters = int(sys.argv[1])
sleep = 1
cpu_count = multiprocessing.cpu_count()
input_params = [
(CpuThread, cpu_n_iters),
(CpuProcess, cpu_n_iters),
(IoThread, sleep),
(IoProcess, sleep),
]
header = ['nthreads']
for thread_class, _ in input_params:
header.append(thread_class.__name__)
print(' '.join(header))
for nthreads in range(1, 2 * cpu_count):
results = [nthreads]
for thread_class, work_size in input_params:
start_time = time.time()
threads = []
for i in range(nthreads):
thread = thread_class(work_size)
threads.append(thread)
thread.start()
for i, thread in enumerate(threads):
thread.join()
results.append(time.time() - start_time)
print(' '.join('{:.6e}'.format(result) for result in results))
GitHub upstream + plotting code on same directory.
Tested on Ubuntu 18.10, Python 3.6.7, in a Lenovo ThinkPad P51 laptop with CPU: Intel Core i7-7820HQ CPU (4 cores / 8 threads), RAM: 2x Samsung M471A2K43BB1-CRC (2x 16GiB), SSD: Samsung MZVLB512HAJQ-000L7 (3,000 MB/s).
Visualize which threads are running at a given time
This post https://rohanvarma.me/GIL/ taught me that you can run a callback whenever a thread is scheduled with the target= argument of threading.Thread and the same for multiprocessing.Process.
This allows us to view exactly which thread runs at each time. When this is done, we would see something like (I made this particular graph up):
+--------------------------------------+
+ Active threads / processes +
+-----------+--------------------------------------+
|Thread 1 |******** ************ |
| 2 | ***** *************|
+-----------+--------------------------------------+
|Process 1 |*** ************** ****** **** |
| 2 |** **** ****** ** ********* **********|
+-----------+--------------------------------------+
+ Time --> +
+--------------------------------------+
which would show that:
- threads are fully serialized by the GIL
- processes can run in parallel
The difference
Thread
Process
Uses native threads, not a native process.
Thread belongs to a process.
Shared memory, not inter-process communication.
Subject to the GIL, not true parallel execution.
Suited to IO-bound tasks, not CPU-bound tasks.
Uses native processes, not native threads.
Process has threads and child processes.
Heavyweight and slower to start, not lightweight and fast to start.
Inter-process communication, not shared memory.
Suited to CPU-bound tasks, probably not IO-bound tasks.
Usage
- When to Use Threads
- Use Threads for IO-Bound
- Reading or writing a file from the hard drive.
- Reading or writing to standard output, input, or error (stdin, stdout, stderr).
- Printing a document.
- Downloading or uploading a file.
- Querying a server.
- Querying a database.
- Taking a photo or recording a video.
- When to Use Processes
- Use the Processes for CPU-Bound Tasks
- Calculating points in a fractal.
- Estimating Pi
- Factoring primes.
- Parsing HTML, JSON, etc. documents.
- Processing text.
- Running simulations.
Source: Thread vs Process in Python
I am learning how to use the threading and the multiprocessing modules in Python to run certain operations in parallel and speed up my code.
I am finding this hard (maybe because I don’t have any theoretical background about it) to understand what the difference is between a threading.Thread() object and a multiprocessing.Process() one.
Also, it is not entirely clear to me how to instantiate a queue of jobs and having only 4 (for example) of them running in parallel, while the other wait for resources to free before being executed.
I find the examples in the documentation clear, but not very exhaustive; as soon as I try to complicate things a bit, I receive a lot of weird errors (like a method that can’t be pickled, and so on).
So, when should I use the threading and multiprocessing modules?
Can you link me to some resources that explain the concepts behind these two modules and how to use them properly for complex tasks?
Multiple threads can exist in a single process.
The threads that belong to the same process share the same memory area (can read from and write to the very same variables, and can interfere with one another).
On the contrary, different processes live in different memory areas, and each of them has its own variables. In order to communicate, processes have to use other channels (files, pipes or sockets).
If you want to parallelize a computation, you’re probably going to need multithreading, because you probably want the threads to cooperate on the same memory.
Speaking about performance, threads are faster to create and manage than processes (because the OS doesn’t need to allocate a whole new virtual memory area), and inter-thread communication is usually faster than inter-process communication. But threads are harder to program. Threads can interfere with one another, and can write to each other’s memory, but the way this happens is not always obvious (due to several factors, mainly instruction reordering and memory caching), and so you are going to need synchronization primitives to control access to your variables.
Well, most of the question is answered by Giulio Franco. I will further elaborate on the consumer-producer problem, which I suppose will put you on the right track for your solution to using a multithreaded app.
fill_count = Semaphore(0) # items produced
empty_count = Semaphore(BUFFER_SIZE) # remaining space
buffer = Buffer()
def producer(fill_count, empty_count, buffer):
while True:
item = produceItem()
empty_count.down();
buffer.push(item)
fill_count.up()
def consumer(fill_count, empty_count, buffer):
while True:
fill_count.down()
item = buffer.pop()
empty_count.up()
consume_item(item)
You could read more on the synchronization primitives from:
http://linux.die.net/man/7/sem_overview
http://docs.python.org/2/library/threading.html
The pseudocode is above. I suppose you should search the producer-consumer-problem to get more references.
What Giulio Franco says is true for multithreading vs. multiprocessing in general.
However, Python* has an added issue: There’s a Global Interpreter Lock that prevents two threads in the same process from running Python code at the same time. This means that if you have 8 cores, and change your code to use 8 threads, it won’t be able to use 800% CPU and run 8x faster; it’ll use the same 100% CPU and run at the same speed. (In reality, it’ll run a little slower, because there’s extra overhead from threading, even if you don’t have any shared data, but ignore that for now.)
There are exceptions to this. If your code’s heavy computation doesn’t actually happen in Python, but in some library with custom C code that does proper GIL handling, like a numpy app, you will get the expected performance benefit from threading. The same is true if the heavy computation is done by some subprocess that you run and wait on.
More importantly, there are cases where this doesn’t matter. For example, a network server spends most of its time reading packets off the network, and a GUI app spends most of its time waiting for user events. One reason to use threads in a network server or GUI app is to allow you to do long-running “background tasks” without stopping the main thread from continuing to service network packets or GUI events. And that works just fine with Python threads. (In technical terms, this means Python threads give you concurrency, even though they don’t give you core-parallelism.)
But if you’re writing a CPU-bound program in pure Python, using more threads is generally not helpful.
Using separate processes has no such problems with the GIL, because each process has its own separate GIL. Of course you still have all the same tradeoffs between threads and processes as in any other languages—it’s more difficult and more expensive to share data between processes than between threads, it can be costly to run a huge number of processes or to create and destroy them frequently, etc. But the GIL weighs heavily on the balance toward processes, in a way that isn’t true for, say, C or Java. So, you will find yourself using multiprocessing a lot more often in Python than you would in C or Java.
Meanwhile, Python’s “batteries included” philosophy brings some good news: It’s very easy to write code that can be switched back and forth between threads and processes with a one-liner change.
If you design your code in terms of self-contained “jobs” that don’t share anything with other jobs (or the main program) except input and output, you can use the concurrent.futures library to write your code around a thread pool like this:
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
executor.submit(job, argument)
executor.map(some_function, collection_of_independent_things)
# ...
You can even get the results of those jobs and pass them on to further jobs, wait for things in order of execution or in order of completion, etc.; read the section on Future objects for details.
Now, if it turns out that your program is constantly using 100% CPU, and adding more threads just makes it slower, then you’re running into the GIL problem, so you need to switch to processes. All you have to do is change that first line:
with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
The only real caveat is that your jobs’ arguments and return values have to be pickleable (and not take too much time or memory to pickle) to be usable cross-process. Usually this isn’t a problem, but sometimes it is.
But what if your jobs can’t be self-contained? If you can design your code in terms of jobs that pass messages from one to another, it’s still pretty easy. You may have to use threading.Thread or multiprocessing.Process instead of relying on pools. And you will have to create queue.Queue or multiprocessing.Queue objects explicitly. (There are plenty of other options—pipes, sockets, files with flocks, … but the point is, you have to do something manually if the automatic magic of an Executor is insufficient.)
But what if you can’t even rely on message passing? What if you need two jobs to both mutate the same structure, and see each others’ changes? In that case, you will need to do manual synchronization (locks, semaphores, conditions, etc.) and, if you want to use processes, explicit shared-memory objects to boot. This is when multithreading (or multiprocessing) gets difficult. If you can avoid it, great; if you can’t, you will need to read more than someone can put into an SO answer.
From a comment, you wanted to know what’s different between threads and processes in Python. Really, if you read Giulio Franco’s answer and mine and all of our links, that should cover everything… but a summary would definitely be useful, so here goes:
- Threads share data by default; processes do not.
- As a consequence of (1), sending data between processes generally requires pickling and unpickling it.**
- As another consequence of (1), directly sharing data between processes generally requires putting it into low-level formats like Value, Array, and
ctypestypes. - Processes are not subject to the GIL.
- On some platforms (mainly Windows), processes are much more expensive to create and destroy.
- There are some extra restrictions on processes, some of which are different on different platforms. See Programming guidelines for details.
- The
threadingmodule doesn’t have some of the features of themultiprocessingmodule. (You can usemultiprocessing.dummyto get most of the missing API on top of threads, or you can use higher-level modules likeconcurrent.futuresand not worry about it.)
* It’s not actually Python, the language, that has this issue, but CPython, the “standard” implementation of that language. Some other implementations don’t have a GIL, like Jython.
** If you’re using the fork start method for multiprocessing—which you can on most non-Windows platforms—each child process gets any resources the parent had when the child was started, which can be another way to pass data to children.
I believe this link answers your question in an elegant way.
To be short, if one of your sub-problems has to wait while another finishes, multithreading is good (in I/O heavy operations, for example); by contrast, if your sub-problems could really happen at the same time, multiprocessing is suggested. However, you won’t create more processes than your number of cores.
Here’s some performance data for python 2.6.x that calls to question the notion that threading is more performant that multiprocessing in IO-bound scenarios. These results are from a 40-processor IBM System x3650 M4 BD.
IO-Bound Processing : Process Pool performed better than Thread Pool
>>> do_work(50, 300, 'thread','fileio')
do_work function took 455.752 ms
>>> do_work(50, 300, 'process','fileio')
do_work function took 319.279 ms
CPU-Bound Processing : Process Pool performed better than Thread Pool
>>> do_work(50, 2000, 'thread','square')
do_work function took 338.309 ms
>>> do_work(50, 2000, 'process','square')
do_work function took 287.488 ms
These aren’t rigorous tests, but they tell me that multiprocessing isn’t entirely unperformant in comparison to threading.
Code used in the interactive python console for the above tests
from multiprocessing import Pool
from multiprocessing.pool import ThreadPool
import time
import sys
import os
from glob import glob
text_for_test = str(range(1,100000))
def fileio(i):
try :
os.remove(glob('./test/test-*'))
except :
pass
f=open('./test/test-'+str(i),'a')
f.write(text_for_test)
f.close()
f=open('./test/test-'+str(i),'r')
text = f.read()
f.close()
def square(i):
return i*i
def timing(f):
def wrap(*args):
time1 = time.time()
ret = f(*args)
time2 = time.time()
print '%s function took %0.3f ms' % (f.func_name, (time2-time1)*1000.0)
return ret
return wrap
result = None
@timing
def do_work(process_count, items, process_type, method) :
pool = None
if process_type == 'process' :
pool = Pool(processes=process_count)
else :
pool = ThreadPool(processes=process_count)
if method == 'square' :
multiple_results = [pool.apply_async(square,(a,)) for a in range(1,items)]
result = [res.get() for res in multiple_results]
else :
multiple_results = [pool.apply_async(fileio,(a,)) for a in range(1,items)]
result = [res.get() for res in multiple_results]
do_work(50, 300, 'thread','fileio')
do_work(50, 300, 'process','fileio')
do_work(50, 2000, 'thread','square')
do_work(50, 2000, 'process','square')
Python documentation quotes
I’ve highlighted the key Python documentation quotes about Process vs Threads and the GIL at: What is the global interpreter lock (GIL) in CPython?
Process vs thread experiments
I did a bit of benchmarking in order to show the difference more concretely.
In the benchmark, I timed CPU and IO bound work for various numbers of threads on an 8 hyperthread CPU. The work supplied per thread is always the same, such that more threads means more total work supplied.
The results were:
Conclusions:
-
for CPU bound work, multiprocessing is always faster, presumably due to the GIL
-
for IO bound work. both are exactly the same speed
-
threads only scale up to about 4x instead of the expected 8x since I’m on an 8 hyperthread machine.
Contrast that with a C POSIX CPU-bound work which reaches the expected 8x speedup: What do 'real', 'user' and 'sys' mean in the output of time(1)?
TODO: I don’t know the reason for this, there must be other Python inefficiencies coming into play.
Test code:
#!/usr/bin/env python3
import multiprocessing
import threading
import time
import sys
def cpu_func(result, niters):
'''
A useless CPU bound function.
'''
for i in range(niters):
result = (result * result * i + 2 * result * i * i + 3) % 10000000
return result
class CpuThread(threading.Thread):
def __init__(self, niters):
super().__init__()
self.niters = niters
self.result = 1
def run(self):
self.result = cpu_func(self.result, self.niters)
class CpuProcess(multiprocessing.Process):
def __init__(self, niters):
super().__init__()
self.niters = niters
self.result = 1
def run(self):
self.result = cpu_func(self.result, self.niters)
class IoThread(threading.Thread):
def __init__(self, sleep):
super().__init__()
self.sleep = sleep
self.result = self.sleep
def run(self):
time.sleep(self.sleep)
class IoProcess(multiprocessing.Process):
def __init__(self, sleep):
super().__init__()
self.sleep = sleep
self.result = self.sleep
def run(self):
time.sleep(self.sleep)
if __name__ == '__main__':
cpu_n_iters = int(sys.argv[1])
sleep = 1
cpu_count = multiprocessing.cpu_count()
input_params = [
(CpuThread, cpu_n_iters),
(CpuProcess, cpu_n_iters),
(IoThread, sleep),
(IoProcess, sleep),
]
header = ['nthreads']
for thread_class, _ in input_params:
header.append(thread_class.__name__)
print(' '.join(header))
for nthreads in range(1, 2 * cpu_count):
results = [nthreads]
for thread_class, work_size in input_params:
start_time = time.time()
threads = []
for i in range(nthreads):
thread = thread_class(work_size)
threads.append(thread)
thread.start()
for i, thread in enumerate(threads):
thread.join()
results.append(time.time() - start_time)
print(' '.join('{:.6e}'.format(result) for result in results))
GitHub upstream + plotting code on same directory.
Tested on Ubuntu 18.10, Python 3.6.7, in a Lenovo ThinkPad P51 laptop with CPU: Intel Core i7-7820HQ CPU (4 cores / 8 threads), RAM: 2x Samsung M471A2K43BB1-CRC (2x 16GiB), SSD: Samsung MZVLB512HAJQ-000L7 (3,000 MB/s).
Visualize which threads are running at a given time
This post https://rohanvarma.me/GIL/ taught me that you can run a callback whenever a thread is scheduled with the target= argument of threading.Thread and the same for multiprocessing.Process.
This allows us to view exactly which thread runs at each time. When this is done, we would see something like (I made this particular graph up):
+--------------------------------------+
+ Active threads / processes +
+-----------+--------------------------------------+
|Thread 1 |******** ************ |
| 2 | ***** *************|
+-----------+--------------------------------------+
|Process 1 |*** ************** ****** **** |
| 2 |** **** ****** ** ********* **********|
+-----------+--------------------------------------+
+ Time --> +
+--------------------------------------+
which would show that:
- threads are fully serialized by the GIL
- processes can run in parallel
The difference
| Thread | Process |
|---|---|
| Uses native threads, not a native process. Thread belongs to a process. Shared memory, not inter-process communication. Subject to the GIL, not true parallel execution. Suited to IO-bound tasks, not CPU-bound tasks. |
Uses native processes, not native threads. Process has threads and child processes. Heavyweight and slower to start, not lightweight and fast to start. Inter-process communication, not shared memory. Suited to CPU-bound tasks, probably not IO-bound tasks. |
Usage
- When to Use Threads
- Use Threads for IO-Bound
- Reading or writing a file from the hard drive.
- Reading or writing to standard output, input, or error (stdin, stdout, stderr).
- Printing a document.
- Downloading or uploading a file.
- Querying a server.
- Querying a database.
- Taking a photo or recording a video.
- Use Threads for IO-Bound
- When to Use Processes
- Use the Processes for CPU-Bound Tasks
- Calculating points in a fractal.
- Estimating Pi
- Factoring primes.
- Parsing HTML, JSON, etc. documents.
- Processing text.
- Running simulations.
- Use the Processes for CPU-Bound Tasks
Source: Thread vs Process in Python