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Concurrent Futures: When and how to implement?

from concurrent.futures import ProcessPoolExecutor
from concurrent.futures import as_completed
import numpy as np
import time
#creating iterable
testDict = {}
for i in range(1000):
testDict[i] = np.random.randint(1,10)
#default method
stime = time.time()
newdict = []
for k, v in testDict.items():
for i in range(1000):
v = np.tanh(v)
newdict.append(v)
etime = time.time()
print(etime - stime)
#output: 1.1139910221099854
#multi processing
stime = time.time()
testresult = []
def f(item):
x = item[1]
for i in range(1000):
x = np.tanh(x)
return x
def main(testDict):
with ProcessPoolExecutor(max_workers = 8) as executor:
futures = [executor.submit(f, item) for item in testDict.items()]
for future in as_completed(futures):
testresult.append(future.result())
if __name__ == '__main__':
main(testDict)
etime = time.time()
print(etime - stime)
#output: 3.4509658813476562
Learning multiprocessing and testing stuff. Ran a test to check if I have implemented this correctly. Looking at the output time taken, concurrent method is 3 times slower. So what's wrong?
My objective is to parallelize a script which mostly operates on a dictionary of around 500 items. Each loop, values of those 500 items are processed and updated. This loops for let's say 5000 generations. None of the k,v pairs interact with other k,v pairs. [Its a genetic algorithm].
I am also looking at guidance on how to parallelize the above described objective. If I use the correct concurrent futures method on each of my function in my genetic algorithm code, where each function takes an input of a dictionary and outputs a new dictionary, will it be useful? Any guides/resources/help is appreciated.
Edit: If I run this example: https://docs.python.org/3/library/concurrent.futures.html#processpoolexecutor-example, it takes 3 times more to solve than a default for loop check.

There are a couple basic problems here, you're using numpy but you're not vectorizing your calculations. You'll not benefit from numpy's speed benefit with the way you write your code here, and might as well just use the standard library math module, which is faster than numpy for this style of code:
# 0.089sec
import math
for k, v in testDict.items():
for i in range(1000):
v = math.tanh(v)
newdict.append(v)
Once you vectorise the operation, only then you see the benefit of numpy:
# 0.016sec
for k, v in testDict.items():
arr = no.full(1000, v)
arr2 = np.tanh(arr)
newdict.append(arr2[-1])
For comparison, your original single threaded code runs in 1.171sec on my test machine. As you can see here, when it's not used properly, NumPy can be a couple orders of magnitude slower than even pure Python.
Now on to why you're seeing what you're seeing.
To be honest, I can't replicate your timing results. Your original multiprocessing code runs in 0.299sec for me macOS on Python 3.6), which is faster than the single process code. But if I have to take a guess, you're probably using Windows? In some platforms like Windows, creating a child process and setting up an environment to run multiprocessing task is very expensive, so using multiprocessing for a task that lasts less than a few seconds is of dubious benefit. If your are interested in why, read here.
Also, in platforms that lacks a usable fork() like MacOS after Python 3.8 or Windows, when you use multiprocessing, the child process has to reimport the module, so if you put both code in the same file, it has to run your single threaded code in the child processes before it can run the multiprocessing code. You'll likely want to put your test code in a function and protect the top level code with if __name__ == "__main__" block. On Mac with Python 3.8 or higher, you can also revert to using fork method by calling multiprocessing.set_start_method("fork") if you're not calling into Mac's non-fork-safe framework libraries.
With that out of the way, on to your title question.
When you use multiprocessing, you need to copy data to the child process and back to the main process to retrieve the result and there's a cost to spawning child processes. To benefit from multiprocessing, you need to design your workload so that this part of the cost is negligible.
If your data comes from external source, try loading the data in the child processes, rather than having the main process load the data then transfer it to the child process, have the main process tell the child how to fetch its slice of data. Here you're generating the testDict in the main process, so if you can, parallelize that and move them to the children instead.
Also, since you're using numpy, if you vectorise your operations properly, numpy will release the GIL while doing vectorised operations, so you may be able to just use multithreading instead. Since numpy doesn't hold GIL during vector operation, you can take advantage of multiple threads in a single Python process, and you don't need to fork or copy data over to child processes, as threads share memory.

dask.delayed results in no speedup

I am trying to get into Dask. For that I attempted to parallelize some time consuming sequential code I got. The original code is this:
def sequential():
sims = []
chunksize = len(tokens)//4
for i in range(0, len(tokens), chunksize):
print(i, i+chunksize)
chunk = tokens[i:i+chunksize]
sims.append(process(chunk))
return sims
%time sequential()
and the prallelized code is this:
def parallel():
sims = []
chunksize = len(tokens)//4
for i in range(0, len(tokens), chunksize):
print(i, i+chunksize)
chunk = dask.delayed(tokens[i:i+chunksize])
sims.append(dask.delayed(process)(chunk))
return dask.delayed(sims)
%time parallel().visualize()
But the parallelized code always runs around 10% slower than the parallel one. when I visualize the computation graph for sims I get this:
Not sure where list-#8 comes from, but other than that it looks correct. So why is there no speedup? When I look into htop I can see 3 cores active (~30% load each), while for the sequential code I see only 1 core active (100% load). The sequential code runs 7 minutes and the parallel code runs 7 - 8 minutes.
I guess I am misunderstanding how delayed and compute should be used here?
The setup is this, if you require it:
import numpy
import spacy
import dask
nlp = spacy.load('en_core_web_lg')
tokens = [t for t in nlp(" ".join(t.strip() for t in open('./words.txt','r').readlines())) if len(t.text) > 1 and len(t.text) < 20]
def process(chunk):
sims = numpy.zeros([len(chunk),len(tokens)], dtype=numpy.float32)
for i in range(len(chunk)):
for j in range(len(tokens)):
sims[i,j] = chunk[i].similarity(tokens[j])
return sims

You are seeing this behaviour because the default execution engine for dask is based on multiple threads in a single process (the "threaded" scheduler). Python has a lock, the GIL, which ensures the safety of the interpreter by only executing one python statement at a time. Therefore, each thread is spending most of its time waiting for the lock to become available.
To avoid this problem, you have two options:
find a version of your computation that releases the GIL. This is possible if you can phrase it as (mainly) some numpy, pandas, numba, etc., computation, code that executes at the C level and doesn't need the interpreter, unlike your nested loops.
run your code using processes, using either the "mutiprocessing" scheduler or (better) the "distributed" scheduler which, despite the name, also runs well on a single machine.
Further information: http://dask.pydata.org/en/latest/scheduler-overview.html

Python Using Multiprocessing

I am trying to use multiprocessing in python 3.6. I have a for loopthat runs a method with different arguments. Currently, it is running one at a time which is taking quite a bit of time so I am trying to use multiprocessing. Here is what I have:
def test(self):
for key, value in dict.items():
pool = Pool(processes=(cpu_count() - 1))
pool.apply_async(self.thread_process, args=(key,value))
pool.close()
pool.join()
def thread_process(self, key, value):
# self.__init__()
print("For", key)
I think what my code is using 3 processes to run one method but I would like to run 1 method per process but I don't know how this is done. I am using 4 cores btw.

You're making a pool at every iteration of the for loop. Make a pool beforehand, apply the processes you'd like to run in multiprocessing, and then join them:
from multiprocessing import Pool, cpu_count
import time
def t():
# Make a dummy dictionary
d = {k: k**2 for k in range(10)}
pool = Pool(processes=(cpu_count() - 1))
for key, value in d.items():
pool.apply_async(thread_process, args=(key, value))
pool.close()
pool.join()
def thread_process(key, value):
time.sleep(0.1) # Simulate a process taking some time to complete
print("For", key, value)
if __name__ == '__main__':
t()

You're not populating your multiprocessing.Pool with data - you're re-initializing the pool on each loop. In your case you can use Pool.map() to do all the heavy work for you:
def thread_process(args):
print(args)
def test():
pool = Pool(processes=(cpu_count() - 1))
pool.map(thread_process, your_dict.items())
pool.close()
if __name__ == "__main__": # important guard for cross-platform use
test()
Also, given all those self arguments I reckon you're snatching this off of a class instance and if so - don't, unless you know what you're doing. Since multiprocessing in Python essentially works as, well, multi-processing (unlike multi-threading) you don't get to share your memory, which means your data is pickled when exchanging between processes, which means anything that cannot be pickled (like instance methods) doesn't get called. You can read more on that problem on this answer.

I think what my code is using 3 processes to run one method but I would like to run 1 method per process but I don't know how this is done. I am using 4 cores btw.
No, you are in fact using the correct syntax here to utilize 3 cores to run an arbitrary function independently on each. You cannot magically utilize 3 cores to work together on one task with out explicitly making that a part of the algorithm itself/ coding that your self often using threads (which do not work the same in python as they do outside of the language).
You are however re-initializing the pool every loop you'll need to do something like this instead to actually perform this properly:
cpus_to_run_on = cpu_count() - 1
pool = Pool(processes=(cpus_to_run_on)
# don't call a dictionary a dict, you will not be able to use dict() any
# more after that point, that's like calling a variable len or abs, you
# can't use those functions now
pool.map(your_function, your_function_args)
pool.close()
Take a look at the python multiprocessing docs for more specific information if you'd like to get a better understanding of how it works. Under python, you cannot utilize threading to do multiprocessing with the default CPython interpreter. This is because of something called the global interpreter lock, which stops concurrent resource access from within python itself. The GIL doesn't exist in other implementations of the language, and is not something other languages like C and C++ have to deal with (and thus you can actually use threads in parallel to work together on a task, unlike CPython)
Python gets around this issue by simply making multiple interpreter instances when using the multiprocessing module, and any message passing between instances is done via copying data between processes (ie the same memory is typically not touched by both interpreter instances). This does not however happen in the misleadingly named threading module, which often actually slow processes down because of a process called context switching. Threading today has limited usefullness, but provides an easier way around non GIL locked processes like socket and file reads/writes than async python.
Beyond all this though there is a bigger problem with your multiprocessing. Your writing to standard output. You aren't going to get the gains you want. Think about it. Each of your processes "print" data, but its all being displayed in one terminal/output screen. So even if your processes are "printing" they aren't really doing that independently, and the information has to be coalesced back into another processes where the text interface lies (ie your console). So these processes write whatever they were going to to some sort of buffer, which then has to be copied (as we learned from how multiprocessing works) to another process which will then take that buffered data and output it.
Typically dummy programs use printing as a means of showing how there is no order between execution of these processes, that they can finish at different times, they aren't meant to demonstrate the performance benefits of multi core processing.

I have experimented a bit this week with multiprocessing. The fastest way that I discovered to do multiprocessing in python3 is using imap_unordered, at least in my scenario. Here is a script you can experiment with using your scenario to figure out what works best for you:
import multiprocessing
NUMBER_OF_PROCESSES = multiprocessing.cpu_count()
MP_FUNCTION = 'imap_unordered' # 'imap_unordered' or 'starmap' or 'apply_async'
def process_chunk(a_chunk):
print(f"processig mp chunk {a_chunk}")
return a_chunk
map_jobs = [1, 2, 3, 4]
result_sum = 0
if MP_FUNCTION == 'imap_unordered':
pool = multiprocessing.Pool(processes=NUMBER_OF_PROCESSES)
for i in pool.imap_unordered(process_chunk, map_jobs):
result_sum += i
elif MP_FUNCTION == 'starmap':
pool = multiprocessing.Pool(processes=NUMBER_OF_PROCESSES)
try:
map_jobs = [(i, ) for i in map_jobs]
result_sum = pool.starmap(process_chunk, map_jobs)
result_sum = sum(result_sum)
finally:
pool.close()
pool.join()
elif MP_FUNCTION == 'apply_async':
with multiprocessing.Pool(processes=NUMBER_OF_PROCESSES) as pool:
result_sum = [pool.apply_async(process_chunk, [i, ]).get() for i in map_jobs]
result_sum = sum(result_sum)
print(f"result_sum is {result_sum}")
I found that starmap was not too far behind in performance, in my scenario it used more cpu and ended up being a bit slower. Hope this boilerplate helps.

Multiprocessing Pool in Python - Only single CPU is utilized

Original Question
I am trying to use multiprocessing Pool in Python. This is my code:
def f(x):
return x
def foo():
p = multiprocessing.Pool()
mapper = p.imap_unordered
for x in xrange(1, 11):
res = list(mapper(f,bar(x)))
This code makes use of all CPUs (I have 8 CPUs) when the xrange is small like xrange(1, 6). However, when I increase the range to xrange(1, 10). I observe that only 1 CPU is running at 100% while the rest are just idling. What could be the reason? Is it because, when I increase the range, the OS shutdowns the CPUs due to overheating?
How can I resolve this problem?
minimal, complete, verifiable example
To replicate my problem, I have created this example: Its a simple ngram generation from a string problem.
#!/usr/bin/python
import time
import itertools
import threading
import multiprocessing
import random
def f(x):
return x
def ngrams(input_tmp, n):
input = input_tmp.split()
if n > len(input):
n = len(input)
output = []
for i in range(len(input)-n+1):
output.append(input[i:i+n])
return output
def foo():
p = multiprocessing.Pool()
mapper = p.imap_unordered
num = 100000000 #100
rand_list = random.sample(xrange(100000000), num)
rand_str = ' '.join(str(i) for i in rand_list)
for n in xrange(1, 100):
res = list(mapper(f, ngrams(rand_str, n)))
if __name__ == '__main__':
start = time.time()
foo()
print 'Total time taken: '+str(time.time() - start)
When num is small (e.g., num = 10000), I find that all 8 CPUs are utilised. However, when num is substantially large (e.g.,num = 100000000). Only 2 CPUs are used and rest are idling. This is my problem.
Caution: When num is too large it may crash your system/VM.

First, ngrams itself takes a lot of time. While that's happening, it's obviously only one one core. But even when that finishes (which is very easy to test by just moving the ngrams call outside the mapper and throwing a print in before and after it), you're still only using one core. I get 1 core at 100% and the other cores all around 2%.
If you try the same thing in Python 3.4, things are a little different—I still get 1 core at 100%, but the others are at 15-25%.
So, what's happening? Well, in multiprocessing, there's always some overhead for passing parameters and returning values. And in your case, that overhead completely swamps the actual work, which is just return x.
Here's how the overhead works: The main process has to pickle the values, then put them on a queue, then wait for values on another queue and unpickle them. Each child process waits on the first queue, unpickles values, does your do-nothing work, pickles the values, and puts them on the other queue. Access to the queues has to be synchronized (by a POSIX semaphore on most non-Windows platforms, I think an NT kernel mutex on Windows).
From what I can tell, your processes are spending over 99% of their time waiting on the queue or reading or writing it.
This isn't too unexpected, given that you have a large amount of data to process, and no computation at all beyond pickling and unpickling that data.
If you look at the source for SimpleQueue in CPython 2.7, the pickling and unpickling happens with the lock held. So, pretty much all the work any of your background processes do happens with the lock held, meaning they all end up serialized on a single core.
But in CPython 3.4, the pickling and unpickling happens outside the lock. And apparently that's enough work to use up 15-25% of a core. (I believe this change happened in 3.2, but I'm too lazy to track it down.)
Still, even on 3.4, you're spending far more time waiting for access to the queue than doing anything, even the multiprocessing overhead. Which is why the cores only get up to 25%.
And of course you're spending orders of magnitude more time on the overhead than the actual work, which makes this not a great test, unless you're trying to test the maximum throughput you can get out of a particular multiprocessing implementation on your machine or something.
A few observations:
In your real code, if you can find a way to batch up larger tasks (explicitly—just relying on chunksize=1000 or the like here won't help), that would probably solve most of your problem.
If your giant array (or whatever) never actually changes, you may be able to pass it in the pool initializer, instead of in each task, which would pretty much eliminate the problem.
If it does change, but only from the main process side, it may be worth sharing rather than passing the data.
If you need to mutate it from the child processes, see if there's a way to partition the data so each task can own a slice without contention.
Even if you need fully-contended shared memory with explicit locking, it may still be better than passing something this huge around.
It may be worth getting a backport of the 3.2+ version of multiprocessing or one of the third-party multiprocessing libraries off PyPI (or upgrading to Python 3.x), just to move the pickling out of the lock.

The problem is that your f() function (which is the one running on separate processes) is doing nothing special, hence it is not putting load on the CPU.
ngrams(), on the other hand, is doing some "heavy" computation, but you are calling this function on the main process, not in the pool.
To make things clearer, consider that this piece of code...
for n in xrange(1, 100):
res = list(mapper(f, ngrams(rand_str, n)))
...is equivalent to this:
for n in xrange(1, 100):
arg = ngrams(rand_str, n)
res = list(mapper(f, arg))
Also the following is a CPU-intensive operation that is being performed on your main process:
num = 100000000
rand_list = random.sample(xrange(100000000), num)
You should either change your code so that sample() and ngrams() are called inside the pool, or change f() so that it does something CPU-intensive, and you'll see a high load on all of your CPUs.

Parfor for Python

I am looking for a definitive answer to MATLAB's parfor for Python (Scipy, Numpy).
Is there a solution similar to parfor? If not, what is the complication for creating one?
UPDATE: Here is a typical numerical computation code that I need speeding up
import numpy as np
N = 2000
output = np.zeros([N,N])
for i in range(N):
for j in range(N):
output[i,j] = HeavyComputationThatIsThreadSafe(i,j)
An example of a heavy computation function is:
import scipy.optimize
def HeavyComputationThatIsThreadSafe(i,j):
n = i * j
return scipy.optimize.anneal(lambda x: np.sum((x-np.arange(n)**2)), np.random.random((n,1)))[0][0,0]

The one built-in to python would be multiprocessing docs are here. I always use multiprocessing.Pool with as many workers as processors. Then whenever I need to do a for-loop like structure I use Pool.imap
As long as the body of your function does not depend on any previous iteration then you should have near linear speed-up. This also requires that your inputs and outputs are pickle-able but this is pretty easy to ensure for standard types.
UPDATE:
Some code for your updated function just to show how easy it is:
from multiprocessing import Pool
from itertools import product
output = np.zeros((N,N))
pool = Pool() #defaults to number of available CPU's
chunksize = 20 #this may take some guessing ... take a look at the docs to decide
for ind, res in enumerate(pool.imap(Fun, product(xrange(N), xrange(N))), chunksize):
output.flat[ind] = res

There are many Python frameworks for parallel computing. The one I happen to like most is IPython, but I don't know too much about any of the others. In IPython, one analogue to parfor would be client.MultiEngineClient.map() or some of the other constructs in the documentation on quick and easy parallelism.

Jupyter Notebook
To see an example consider you want to write the equivalence of this Matlab code on in Python
matlabpool open 4
parfor n=0:9
for i=1:10000
for j=1:10000
s=j*i
end
end
n
end
disp('done')
The way one may write this in python particularly in jupyter notebook. You have to create a function in the working directory (I called it FunForParFor.py) which has the following
def func(n):
for i in range(10000):
for j in range(10000):
s=j*i
print(n)
Then I go to my Jupyter notebook and write the following code
import multiprocessing
import FunForParFor
if __name__ == '__main__':
pool = multiprocessing.Pool(processes=4)
pool.map(FunForParFor.func, range(10))
pool.close()
pool.join()
print('done')
This has worked for me! I just wanted to share it here to give you a particular example.

This can be done elegantly with Ray, a system that allows you to easily parallelize and distribute your Python code.
To parallelize your example, you'd need to define your functions with the #ray.remote decorator, and then invoke them with .remote.
import numpy as np
import time
import ray
ray.init()
# Define the function. Each remote function will be executed
# in a separate process.
#ray.remote
def HeavyComputationThatIsThreadSafe(i, j):
n = i*j
time.sleep(0.5) # Simulate some heavy computation.
return n
N = 10
output_ids = []
for i in range(N):
for j in range(N):
# Remote functions return a future, i.e, an identifier to the
# result, rather than the result itself. This allows invoking
# the next remote function before the previous finished, which
# leads to the remote functions being executed in parallel.
output_ids.append(HeavyComputationThatIsThreadSafe.remote(i,j))
# Get results when ready.
output_list = ray.get(output_ids)
# Move results into an NxN numpy array.
outputs = np.array(output_list).reshape(N, N)
# This program should take approximately N*N*0.5s/p, where
# p is the number of cores on your machine, N*N
# is the number of times we invoke the remote function,
# and 0.5s is the time it takes to execute one instance
# of the remote function. For example, for two cores this
# program will take approximately 25sec.
There are a number of advantages of using Ray over the multiprocessing module. In particular, the same code will run on a single machine as well as on a cluster of machines. For more advantages of Ray see this related post.
Note: One point to keep in mind is that each remote function is executed in a separate process, possibly on a different machine, and thus the remote function's computation should take more than invoking a remote function. As a rule of thumb a remote function's computation should take at least a few 10s of msec to amortize the scheduling and startup overhead of a remote function.

I've always used Parallel Python but it's not a complete analog since I believe it typically uses separate processes which can be expensive on certain operating systems. Still, if the body of your loops are chunky enough then this won't matter and can actually have some benefits.

I tried all solutions here, but found that the simplest way and closest equivalent to matlabs parfor is numba's prange.
Essentially you change a single letter in your loop, range to prange:
from numba import autojit, prange
#autojit
def parallel_sum(A):
sum = 0.0
for i in prange(A.shape[0]):
sum += A[i]
return sum

I recommend trying joblib Parallel.
one liner
from joblib import Parallel, delayed
out = Parallel(n_jobs=2)(delayed(heavymethod)(i) for i in range(10))
instructional
instead of taking a for loop
from time import sleep
for _ in range(10):
sleep(.2)
rewrite your operation into a list comprehension
[sleep(.2) for _ in range(10)]
Now let us not directly evaluate the expression, but collect what should be done.
This is what the delayed method is for.
from joblib import delayed
[delayed(sleep(.2)) for _ in range(10)]
Next instantiate a parallel process with n_workers and process the list.
from joblib import Parallel
r = Parallel(n_jobs=2, verbose=10)(delayed(sleep)(.2) for _ in range(10))
[Parallel(n_jobs=2)]: Done 1 tasks | elapsed: 0.6s
[Parallel(n_jobs=2)]: Done 4 tasks | elapsed: 0.8s
[Parallel(n_jobs=2)]: Done 10 out of 10 | elapsed: 1.4s finished

Ok, I'll also give it a go, let's see if my way is easier
from multiprocessing import Pool
def heavy_func(key):
#do some heavy computation on each key
output = key**2
return key, output
output_data ={} #<--this dict will store the results
keys = [1,5,7,8,10] #<--compute heavy_func over all the values of keys
with Pool(processes=40) as pool:
for i in pool.imap_unordered(heavy_func, keys):
output_data[i[0]] = i[1]
Now output_data is a dictionary that will contain for every key the result of the computation on this key.
That is it..