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Why does python multiprocessing run sequentially?

I try to speeds up the dot product of two large matrice, so I test a small example of multiprocessing. The codes are as follows. But from the results, I found that my codes runs like sequentially.
Codes
import multiprocessing as mp
import numpy as np
import time
def dot(i):
print(f"Process {i} enters")
np.random.seed(10)
a = np.random.normal(0, 1, (5000, 5000))
b = np.random.normal(0, 1, (5000, 5000))
print(f"Process {i} starts calculating")
res = np.dot(a, b)
print(f"Process {i} finishes")
return res
if __name__ == '__main__':
start = time.perf_counter()
dot(1)
print(time.perf_counter() - start)
print('=============================')
print(mp.cpu_count())
i = 8
start = time.perf_counter()
pool = mp.Pool(mp.cpu_count())
res = []
for j in range(i):
res.append(pool.apply_async(dot, args=(j,)))
pool.close()
pool.join()
end = time.perf_counter()
# res = [r.get() for r in res]
# print(res)
print(end - start)
Results
Process 1 enters
Process 1 starts calculating
Process 1 finishes
2.582571708
=============================
8
Process 0 enters
Process 1 enters
Process 2 enters
Process 3 enters
Process 4 enters
Process 5 enters
Process 6 enters
Process 7 enters
Process 4 starts calculating
Process 7 starts calculating
Process 5 starts calculating
Process 3 starts calculating
Process 1 starts calculating
Process 6 starts calculating
Process 0 starts calculating
Process 2 starts calculating
Process 4 finishes
Process 7 finishes
Process 1 finishes
Process 0 finishes
Process 6 finishes
Process 2 finishes
Process 5 finishes
Process 3 finishes
27.05124225
The results showed that the codes seems to run indeed parallelly (from the text), but the final running time seems run sequentially. I don't know why, so hope some one could give me some advice. Thanks in advance.

Of course there is always additional overhead involved in creating processes and in passing arguments and results between address spaces (and in this case your results are extremely large).
My best guess is that the performance problem is arising because the storage requirements for running 8 processes in parallel (I assume you have at least 8 logical processors, preferably 8 physical processors) due to the large arrays being computed is probably causing extreme paging (I get the same results as you). I have therefore modified the demo to be less memory intensive but kept the CPU requirements high by performing the dot function many times in a loop. I have also reduced the number of processes to 4, which is the number of physical processors that I have on my desktop, which gives each process a better chance of running in parallel:
from multiprocessing.pool import Pool
import numpy as np
import time
def dot(i):
print(f"Process {i} enters")
np.random.seed(10)
a = np.random.normal(0, 1, (50, 50))
b = np.random.normal(0, 1, (50, 50))
print(f"Process {i} starts calculating")
for _ in range(500_000):
res = np.dot(a, b)
print(f"Process {i} finishes")
return res
if __name__ == '__main__':
start = time.perf_counter()
dot(1)
print(time.perf_counter() - start)
print('=============================')
i = 4
start = time.perf_counter()
pool = Pool(i)
res = []
for j in range(i):
res.append(pool.apply_async(dot, args=(j,)))
pool.close()
pool.join()
end = time.perf_counter()
# res = [r.get() for r in res]
# print(res)
print(end - start)
Results:
Process 1 enters
Process 1 starts calculating
Process 1 finishes
6.0469717
=============================
Process 0 enters
Process 0 starts calculating
Process 1 enters
Process 1 starts calculating
Process 2 enters
Process 3 enters
Process 2 starts calculating
Process 3 starts calculating
Process 0 finishes
Process 1 finishes
Process 3 finishes
Process 2 finishes
8.8419177
This is much closer to what you would expect. When I change i to 8, the number of logical processors, then the running times were 6.1023760000000005 and 12.749368100000002 respectively.

how to extract a variable in the middle of process to another process multiprocessing python

i made a program in python that runs 2 processes simultaneously, but i want to take a variable in the middle of the first process to the other process, like passing that variable from function to function. Here is a code that is working but the output is not what i expected.
import multiprocessing
import time
t=0
def countup():
global t
while t<25:
t=t+1
time.sleep(1)
print("count",t)
def what():
global t
globals().update()
while True:
time.sleep(3)
print ("the count reached ",t)
if __name__=="__main__":
p1 = multiprocessing.Process(target=countup,args=())
p2 = multiprocessing.Process(target=what, args=())
p1.start()
p2.start()
p1.join()
p2.join()
the output is shown as this
count 1
count 2
the count reached 0
count 3
count 4
count 5
the count reached 0
count 6
count 7
count 8
the count reached 0
...
the variable "t" is not updating, but both processes are working at the same time. The expected results suppose to be like this:
count 1
count 2
the count reached 2
count 3
count 4
count 5
the count reached 5
count 6
count 7
count 8
the count reached 6
...
am i missing something ? is there something wrong in my code?

i solved it , according to #deets , using Queuing or pipes to exchange variables is the key.
from multiprocessing import Process, Queue
def f(q):
q.put([42, None, 'hello'])
if __name__ == '__main__':
q = Queue()
p = Process(target=f, args=(q,))
p.start()
print(q.get()) # prints "[42, None, 'hello']"
p.join()
amazing that i used this method for synchronized count printing between 2 different processes that work simultaneously. ... just wow
here is the code:
import multiprocessing
from multiprocessing import Process,Queue
import time
t=0
def countup(q):
global t
while t<25:
t=t+1
time.sleep(1)
print("count",t)
q.put(t)
if __name__=="__main__":
q = Queue()
p = multiprocessing.Process(target=countup,args=(q,))
p.start()
while (q.get()<15):
time.sleep(2)
print("the count reached ",q.get())
p.join()
the result is this
count 1
count 2
the count reached 2
count 3
count 4
count 5
the count reached 4
count 6
the count reached 6
count 7
count 8
...

Python Multiprocessing reading input iterator all at once

Using python 3.4.3, I have a generator function foo that yields data to be processed in parallel. Passing this function to multiprocessing.Pool.map of n processes, I expected it to be called n times at a time:
from multiprocessing import Pool
import time
now = time.time
def foo(n):
for i in range(n):
print("%f get %d" % (now(), i))
yield i
def bar(i):
print("%f start %d" % (now(), i))
time.sleep(1)
print("%f end %d" % (now(), i))
pool = Pool(2)
pool.map(bar, foo(6))
pool.close()
pool.join()
Unfortunately, the generator function is called 6 times immediately. The output is this:
1440713274.290760 get 0
1440713274.290827 get 1
1440713274.290839 get 2
1440713274.290849 get 3
1440713274.290858 get 4
1440713274.290867 get 5
1440713274.291526 start 0
1440713274.291654 start 1
1440713275.292680 end 0
1440713275.292803 end 1
1440713275.293056 start 2
1440713275.293129 start 3
1440713276.294106 end 2
1440713276.294182 end 3
1440713276.294344 start 4
1440713276.294390 start 5
1440713277.294803 end 4
1440713277.294859 end 5
But I had hoped to get something more like:
1440714272.612041 get 0
1440714272.612078 get 1
1440714272.612090 start 0
1440714272.612100 start 1
1440714273.613174 end 0
1440714273.613247 end 1
1440714273.613264 get 2
1440714273.613276 get 3
1440714273.613287 start 2
1440714273.613298 start 3
1440714274.614357 end 2
1440714274.614423 end 3
1440714274.614432 get 4
1440714274.614437 get 5
1440714274.614443 start 4
1440714274.614448 start 5
1440714275.615475 end 4
1440714275.615549 end 5
(Reason is that foo is going to read a large amount of data into memory.)
I got the same results with pool.imap(bar, foo(6), 2) and
for i in foo(6):
pool.apply_async(bar, args=(i,))
What is the easiest way to make this work?

I had faced a similar problem, where I needed to read a large amount of data and process parts of it in parallel. I solved it by sub-classing the multiprocessing.Process and using queues. I think you will benefit from reading about embarrassingly parallel problems. I have given sample code below:
import multiprocessing
import time
import logging
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M:%S')
#Producer class
class foo(multiprocessing.Process):
def __init__(self, n, queues):
super(foo, self).__init__()
self.n=n
self.queues = queues
def run(self):
logging.info('Starting foo producer')
for i in range(self.n):
logging.info('foo: Sending "%d" to a consumer' % (i))
self.queues[i%len(self.queues)].put(i)
time.sleep(1)#Unnecessary sleep to demonstrate order of events
for q in self.queues:
q.put('end')
logging.info('Ending foo producer')
return
#Consumer class
class bar(multiprocessing.Process):
def __init__(self, idx, queue):
super(bar, self).__init__()
self.idx = idx
self.queue = queue
def run(self):
logging.info("Starting bar %d consumer" % (self.idx ))
while True:
fooput = self.queue.get()
if type(fooput)==str and fooput=='end':
break
logging.info('bar %d: Got "%d" from foo' % (self.idx, fooput))
time.sleep(2)#Unnecessary sleep to demonstrate order of events
logging.info("Ending bar %d consumer" % (self.idx ))
return
if __name__=='__main__':
#make queues to put data read by foo
count_queues = 2
queues =[]
for i in range(count_queues):
q = multiprocessing.Queue(2)
# Give queue size according to your buffer requirements
queues.append(q)
#make reader for reading data. lets call this object Producer
foo_object = foo(6, queues)
#make receivers for the data. Lets call these Consumers
#Each consumer is assigned a queue
bar_objects = []
for idx, q in enumerate(queues):
bar_object = bar(idx, q)
bar_objects.append(bar_object)
# start the consumer processes
for bar_object in bar_objects:
bar_object.start()
# start the producer processes
foo_object.start()
#Join all started processes
for bar_object in bar_objects:
bar_object.join()
foo_object.join()

The best I can come up with myself is this:
pool_size = 2
pool = Pool(pool_size)
count = 0
for i in foo(6):
count += 1
if count % pool_size == 0:
pool.apply(bar, args=(i,))
else:
pool.apply_async(bar, args=(i,))
pool.close()
pool.join()
for pool_size=2 it outputs:
1440798963.389791 get 0
1440798963.490108 get 1
1440798963.490683 start 0
1440798963.595587 start 1
1440798964.491828 end 0
1440798964.596687 end 1
1440798964.597137 get 2
1440798964.697373 get 3
1440798964.697629 start 2
1440798964.798024 start 3
1440798965.698719 end 2
1440798965.799108 end 3
1440798965.799419 get 4
1440798965.899689 get 5
1440798965.899984 start 4
1440798966.001016 start 5
1440798966.901050 end 4
1440798967.002097 end 5
for pool_size=3 it outputs:
1440799101.917546 get 0
1440799102.018438 start 0
1440799102.017869 get 1
1440799102.118868 get 2
1440799102.119903 start 1
1440799102.219616 start 2
1440799103.019600 end 0
1440799103.121066 end 1
1440799103.220746 end 2
1440799103.221124 get 3
1440799103.321402 get 4
1440799103.321664 start 3
1440799103.422589 get 5
1440799103.422824 start 4
1440799103.523286 start 5
1440799104.322934 end 3
1440799104.423878 end 4
1440799104.524350 end 5
However, it would take 3 new items from the iterator as soon as the apply finishes. If the processing takes variable time, this won't work as well.

python generators for concurrency

I am following slides from Python's guru David Beazley. It states "Generators are also used for concurrency. Here is an example:
from collections import deque
def countdown(n):
while n > 0:
print("T-minus", n)
yield
n -=1
def countup(n):
x = 0
while x > n:
print("Up we go", x)
yield
x +=1
# instantiate some tasks in a queue
tasks = deque([countdown(10),
countdown(5),
countup(20)
])
# run a little scheduler
while tasks:
t = tasks.pop() # get a task
try:
next(t) # run it until it yields
tasks.appendleft(t) # reschedule
except StopIteration:
pass
Here is the output:
T-minus 5
T-minus 10
T-minus 4
T-minus 9
T-minus 3
T-minus 8
T-minus 2
T-minus 7
T-minus 1
T-minus 6
T-minus 5
T-minus 4
T-minus 3
T-minus 2
T-minus 1
The question is how is concurrency introduced by generators and how is it manifesting?

This bit of code implements the concept of "green threads", cooperative, userland (as opposed to Preemptive, kernel) threading.
The "threads" are the generators, each function with yeild or yield from in it. The scheduler lives, obviously, inside the if __name__ == '__main__': bit.
So, lets imagine we have not generators but regular lists, and each list has in it, a sequence of functions.
def doThis(): pass
def sayThis(): pass
def doThat(): pass
...
myThread = [doThis, doThat, doAnother]
yourThread = [sayThis, sayThat, sayAnother]
We could run all of the functions in order:
for thread in [myThread, yourThread]:
for stmt in thread:
stmt()
Or we could do them in some other order:
for myStmt, yourStmt in zip(myThread, yourThread):
myStmt()
yourStmt()
In the first "scheduler", we exhaust the first thread, and then proceed to the second thread. In the second scheduler, we interleave statements out of both threads, first mine, then yours, then back to mine.
It's because we are interleaving "statements" across multiple "threads" before exausting those threads that we can say that the second scheduler gives concurrency.
Note that concurrency doesn't neccesarily mean parallelism. It's not simultaneous execution, just overlapping.

Here is an example to clarify:
from collections import deque
def coro1():
for i in range(1, 10):
yield i
def coro2():
for i in range(1, 10):
yield i*10
print('Async behaviour'.center(60, '#'))
tasks = deque()
tasks.extend([coro1(), coro2()])
while tasks:
task = tasks.popleft() # select and remove a task (coro1/coro2).
try:
print(next(task))
tasks.append(task) # add the removed task (coro1/coro2) for permutation.
except StopIteration:
pass
Out:
######################Async behaviour#######################
1
10
2
20
3
30
4
40
5
50
6
60
7
70
8
80
9
90

running multiple processes simultaneously

I am attempting to create a program in python that runs multiple instances (15) of a function simultaneously over different processors. I have been researching this, and have the below program set up using the Process tool from multiprocessing.
Unfortunately, the program executes each instance of the function sequentially (it seems to wait for one to finish before moving onto the next part of the loop).
from __future__ import print_function
from multiprocessing import Process
import sys
import os
import re
for i in range(1,16):
exec("path%d = 0" % (i))
exec("file%d = open('%d-path','a', 1)" % (i, i))
def stat(first, last):
for j in range(1,40000):
input_string = "water" + str(j) + ".xyz.geocard"
if os.path.exists('./%s' % input_string) == True:
exec("out%d = open('output%d', 'a', 1)" % (first, first))
exec('print("Processing file %s...", file=out%d)' % (input_string, first))
with open('./%s' % input_string,'r') as file:
for line in file:
for i in range(first,last):
search_string = " " + str(i) + " path:"
for result in re.finditer(r'%s' % search_string, line):
exec("path%d += 1" % i)
for i in range(first,last):
exec("print(path%d, file=file%d)" % (i, i))
processes = []
for m in range(1,16):
n = m + 1
p = Process(target=stat, args=(m, n))
p.start()
processes.append(p)
for p in processes:
p.join()
I am reasonably new to programming, and have no experience with parallelization - any help would be greatly appreciated.
I have included the entire program above, replacing "Some Function" with the actual function, to demonstrate that this is not a timing issue. The program can take days to cycle through all 40,000 files (each of which is quite large).

I think what is happening is that you are not doing enough in some_function to observe work happening in parallel. It spawns a process, and it completes before the next one gets spawned. If you introduce a random sleep time into some_function, you'll see that they are in fact running in parallel.
from multiprocessing import Process
import random
import time
def some_function(first, last):
time.sleep(random.randint(1, 3))
print first, last
processes = []
for m in range(1,16):
n = m + 1
p = Process(target=some_function, args=(m, n))
p.start()
processes.append(p)
for p in processes:
p.join()
Output
2 3
3 4
5 6
12 13
13 14
14 15
15 16
1 2
4 5
6 7
9 10
8 9
7 8
11 12
10 11

Are you sure? I just tried it and it worked for me; the results are out of order on every execution, so they're being executed concurrently.
Have a look at your function. It takes "first" and "last", so is its execution time smaller for lower values? In this case, you could expect the smaller numbered arguments to make runtime lower, so it would appear to run in parallel.
ps ux | grep python | grep -v grep | wc -l
> 16
If you execute the code repeatedly (i.e. using a bash script) you can see that every process is starting up. If you want to confirm this, import os and have the function print out os.getpid() so you can see they have a different process ID.
So yeah, double check your results because it seems to me like you've written it concurrently just fine!

This code below can run 10 processes parallelly printing the numbers from 0 to 99.
*if __name__ == "__main__":
is needed to run processes on Windows:
from multiprocessing import Process
def test():
for i in range(0, 100):
print(i)
if __name__ == "__main__": # Here
process_list = []
for _ in range(0, 10):
process = Process(target=test)
process_list.append(process)
for process in process_list:
process.start()
for process in process_list:
process.join()
And, this code below is the shorthand for loop version of the above code running 10 processes parallelly printing the numbers from 0 to 99:
from multiprocessing import Process
def test():
[print(i) for i in range(0, 100)]
if __name__ == "__main__":
process_list = [Process(target=test) for _ in range(0, 10)]
[process.start() for process in process_list]
[process.join() for process in process_list]
This is the result below:
...
99
79
67
71
67
89
81
99
80
68
...