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Why is my parallel code slower than my serial code?

Recently started learning parallel on my own and I have next to no idea what I'm doing. Tried applying what I have learnt but I think I'm doing something wrong because my parallel code is taking a longer time to execute than my serial code. My PC is running a i7-9700. This is the original serial code in question
def getMatrix(name):
matrixCreated = []
i = 0
while True:
i += 1
row = input('\nEnter elements in row %s of Matrix %s (separated by commas)\nOr -1 to exit: ' %(i, name))
if row == '-1':
break
else:
strList = row.split(',')
matrixCreated.append(list(map(int, strList)))
return matrixCreated
def getColAsList(matrixToManipulate, col):
myList = []
numOfRows = len(matrixToManipulate)
for i in range(numOfRows):
myList.append(matrixToManipulate[i][col])
return myList
def getCell(matrixA, matrixB, r, c):
matrixBCol = getColAsList(matrixB, c)
lenOfList = len(matrixBCol)
productList = [matrixA[r][i]*matrixBCol[i] for i in range(lenOfList)]
return sum(productList)
matrixA = getMatrix('A')
matrixB = getMatrix('B')
rowA = len(matrixA)
colA = len(matrixA[0])
rowB = len(matrixB)
colB = len(matrixB[0])
result = [[0 for p in range(colB)] for q in range(rowA)]
if (colA != rowB):
print('The two matrices cannot be multiplied')
else:
print('\nThe result is')
for i in range(rowA):
for j in range(colB):
result[i][j] = getCell(matrixA, matrixB, i, j)
print(result[i])
EDIT: This is the parallel code with time library. Initially didn't include it as I thought it was wrong so just wanted to see if anyone had ideas to parallize it instead
import multiprocessing as mp
pool = mp.Pool(mp.cpu_count())
def getMatrix(name):
matrixCreated = []
i = 0
while True:
i += 1
row = input('\nEnter elements in row %s of Matrix %s (separated by commas)\nOr -1 to exit: ' %(i, name))
if row == '-1':
break
else:
strList = row.split(',')
matrixCreated.append(list(map(int, strList)))
return matrixCreated
def getColAsList(matrixToManipulate, col):
myList = []
numOfRows = len(matrixToManipulate)
for i in range(numOfRows):
myList.append(matrixToManipulate[i][col])
return myList
def getCell(matrixA, matrixB, r, c):
matrixBCol = getColAsList(matrixB, c)
lenOfList = len(matrixBCol)
productList = [matrixA[r][i]*matrixBCol[i] for i in range(lenOfList)]
return sum(productList)
matrixA = getMatrix('A')
matrixB = getMatrix('B')
rowA = len(matrixA)
colA = len(matrixA[0])
rowB = len(matrixB)
colB = len(matrixB[0])
import time
start_time = time.time()
result = [[0 for p in range(colB)] for q in range(rowA)]
if (colA != rowB):
print('The two matrices cannot be multiplied')
else:
print('\nThe result is')
for i in range(rowA):
for j in range(colB):
result[i][j] = getCell(matrixA, matrixB, i, j)
print(result[i])
print (" %s seconds " % (time.time() - start_time))
results = [pool.apply(getMatrix, getColAsList, getCell)]
pool.close()

So I would agree that you are doing something wrong. I would say that your code is not parallelable.
For the code to be parallelable it has to be dividable into smaller pieces and it either has to be:
1, Independent, meaning when it runs it doesn't rely on other processes to do its job.
For example if I have a list with 1,000,000 objects that need to be processed. And I have 4 workers to process them with. Then give each worker 1/4 of the objects to process and then when they finish all objects have been processed. But worker 3 doesn't care if worker 1, 2 or 4 completed before or after it did. Nor does worker3 care about what worker 1, 2 or 4 returned or did. It actually shouldn't even know that there are any other workers out there.
2, Managed, meaning there is dependencies between workers but thats ok cause you have a main thread that coordinates the workers. Still though, workers shouldn't know or care about each other. Think of them as mindless muscle, they only do what you tell them to do. Not to think for themselves.
For example I have a list with 1,000,000 objects that need to be processed. First all objects need to go through func1 which returns something. Once ALL objects are done with func1 those results should then go into func2. So I create 4 workers, give each worker 1/4 of the objects and have them process them with func1 and return the results. I wait for all workers to finish processing the objects. Then I give each worker 1/4 of the results returned by func1 and have them process it with func2. And I can keep doing this as many times as I want. All I have to do is have the main thread coordinate the workers so they dont start when they aren't suppose too and tell them what and when to process.
Take this with a grain of salt as this is a simplified version of parallel processing.
Tip for parallel and concurrency
You shouldn't get user input in parallel. Only the main thread should handle that.
If your work load is light then you shouldn't use parallel processing.
If your task can't be divided up into smaller pieces then its not parallelable. But it can still be run on a background thread as a way of running something concurrently.
Concurrency Example:
If your task is long running and not parallelable, lets say it takes 10 minutes to complete. And it requires a user to give input. Then when the user gives input start the task on a worker. If the user gives input again 1 minute later then take that input and start the 2nd task on worker2. Input at 5 minutes start task3 on worker3. At the 10 minute mark task1 is complete. Because everything is running concurrently by the 15 minute mark all task are complete. That's 2x faster then running the tasks in serial which would take 30 minutes. However this is concurrency not parallel.

Can't use Python multiprocessing with large amount of calculations

I have to speed up my current code to do around 10^6 operations in a feasible time. Before I used multiprocessing in my the actual document I tried to do it in a mock case. Following is my attempt:
def chunkIt(seq, num):
avg = len(seq) / float(num)
out = []
last = 0.0
while last < len(seq):
out.append(seq[int(last):int(last + avg)])
last += avg
return out
def do_something(List):
# in real case this function takes about 0.5 seconds to finish for each
iteration
turn = []
for e in List:
turn.append((e[0]**2, e[1]**2,e[2]**2))
return turn
t1 = time.time()
List = []
#in the real case these 20's can go as high as 150
for i in range(1,20-2):
for k in range(i+1,20-1):
for j in range(k+1,20):
List.append((i,k,j))
t3 = time.time()
test = []
List = chunkIt(List,3)
if __name__ == '__main__':
with concurrent.futures.ProcessPoolExecutor() as executor:
results = executor.map(do_something,List)
for result in results:
test.append(result)
test= np.array(test)
t2 = time.time()
T = t2-t1
T2 = t3-t1
However, when I increase the size of my "List" my computer tires to use all of my RAM and CPU and freezes. I even cut my "List" into 3 pieces so it will only use 3 of my cores. However, nothing changed. Also, when I tried to use it on a smaller data set I noticed the code ran much slower than when it ran on a single core.
I am still very new to multiprocessing in Python, am I doing something wrong. I would appreciate it if you could help me.

To reduce memory usage, I suggest you use instead the multiprocessing module and specifically the imap method method (or imap_unordered method). Unlike the map method of either multiprocessing.Pool or concurrent.futures.ProcessPoolExecutor, the iterable argument is processed lazily. What this means is that if you use a generator function or generator expression for the iterable argument, you do not need to create the complete list of arguments in memory; as a processor in the pool become free and ready to execute more tasks, the generator will be called upon to generate the next argument for the imap call.
By default a chunksize value of 1 is used, which can be inefficient for a large iterable size. When using map and the default value of None for the chunksize argument, the pool will look at the length of the iterable first converting it to a list if necessary and then compute what it deems to be an efficient chunksize based on that length and the size of the pool. When using imap or imap_unordered, converting the iterable to a list would defeat the whole purpose of using that method. But if you know what that size would be (more or less) if the iterable were converted to a list, then there is no reason not to apply the same chunksize calculation the map method would have, and that is what is done below.
The following benchmarks perform the same processing first as a single process and then using multiprocessing using imap where each invocation of do_something on my desktop takes approximately .5 seconds. do_something now has been modified to just process a single i, k, j tuple as there is no longer any need to break up anything into smaller lists:
from multiprocessing import Pool, cpu_count
import time
def half_second():
HALF_SECOND_ITERATIONS = 10_000_000
sum = 0
for _ in range(HALF_SECOND_ITERATIONS):
sum += 1
return sum
def do_something(tpl):
# in real case this function takes about 0.5 seconds to finish for each iteration
half_second() # on my desktop
return tpl[0]**2, tpl[1]**2, tpl[2]**2
"""
def generate_tpls():
for i in range(1, 20-2):
for k in range(i+1, 20-1):
for j in range(k+1, 20):
yield i, k, j
"""
# Use smaller number of tuples so we finish in a reasonable amount of time:
def generate_tpls():
# 64 tuples:
for i in range(1, 5):
for k in range(1, 5):
for j in range(1, 5):
yield i, k, j
def benchmark1():
""" single processing """
t = time.time()
for tpl in generate_tpls():
result = do_something(tpl)
print('benchmark1 time:', time.time() - t)
def compute_chunksize(iterable_size, pool_size):
""" This is more-or-less the function used by the Pool.map method """
chunksize, remainder = divmod(iterable_size, 4 * pool_size)
if remainder:
chunksize += 1
return chunksize
def benchmark2():
""" multiprocssing """
t = time.time()
pool_size = cpu_count() # 8 logical cores (4 physical cores)
N_TUPLES = 64 # number of tuples that will be generated
pool = Pool(pool_size)
chunksize = compute_chunksize(N_TUPLES, pool_size)
for result in pool.imap(do_something, generate_tpls(), chunksize=chunksize):
pass
print('benchmark2 time:', time.time() - t)
if __name__ == '__main__':
benchmark1()
benchmark2()
Prints:
benchmark1 time: 32.261038303375244
benchmark2 time: 8.174998044967651

The nested For loops creating the array before the main definition appears to be the problem. Moving that part to underneath the main definition clears up any memory problems.
def chunkIt(seq, num):
avg = len(seq) / float(num)
out = []
last = 0.0
while last < len(seq):
out.append(seq[int(last):int(last + avg)])
last += avg
return out
def do_something(List):
# in real case this function takes about 0.5 seconds to finish for each
iteration
turn = []
for e in List:
turn.append((e[0]**2, e[1]**2,e[2]**2))
return turn
if __name__ == '__main__':
t1 = time.time()
List = []
#in the real case these 20's can go as high as 150
for i in range(1,20-2):
for k in range(i+1,20-1):
for j in range(k+1,20):
List.append((i,k,j))
t3 = time.time()
test = []
List = chunkIt(List,3)
with concurrent.futures.ProcessPoolExecutor() as executor:
results = executor.map(do_something,List)
for result in results:
test.append(result)
test= np.array(test)
t2 = time.time()
T = t2-t1
T2 = t3-t1

Using Multithreading or Multiprocessing to improve computational speed

I am iterating through very large file size [mesh]. Since the iteration is independent, I would like to split my mesh into smaller sizes and run them all at the same time in order to lower computation time. Below is a sample code. For example, if mesh is of length=50000, I would like to divide the mesh into 100 and run fun for each mesh/100 at the same time.
import numpy as np
def fnc(data, mesh):
d = []
for i, dummy_val in enumerate(mesh):
d.append(np.sqrt((data[:, 0]-mesh[i, 0])**2.0 + (data[:, 1]-mesh[i, 1])**2.0))
return d
interpolate = fnc(mydata, mymesh)
I would like to know to achieve this using multiprocessing or multithreading as I'm unable to reconcile it with the execution of my loop.

This will give you the general idea. I couldn't test this since I do not have your data. The default constructor for ProcessPoolExecutor will use the number of processors on your computer. But since that determines the level of multiprocessing you can have, it will probably be more efficient to set the N_CHUNKS parameter to the number of simultaneous processes you can support. That is, if you have a processing pool size of 6, then it is better to just divide your array into 6 large chunks and have 6 processes do the work rather than breaking it up into smaller pieces where processes will have to wait to run. So you should probably specify a specific max_workers number to the ProcessPoolExecutor not greater than the number of processors you have and set N_CHUNKS to the same value.
from concurrent.futures import ProcessPoolExecutor, as_completed
import numpy as np
def fnc(data, mesh):
d = []
for i, dummy_val in enumerate(mesh):
d.append(np.sqrt((data[:, 0]-mesh[i, 0])**2.0 + (data[:, 1]-mesh[i, 1])**2.0))
return d
def main(data, mesh):
#N_CHUNKS = 100
N_CHUNKS = 6 # assuming you have 6 processors; see max_workers parameter
n = len(mesh)
assert n != 0
if n <= N_CHUNKS:
N_CHUNKS = 1
chunk_size = n
last_chunk_size = n
else:
chunk_size = n // N_CHUNKS
last_chunk_size = n - chunk_size * (N_CHUNKS - 1)
with ProcessPoolExcutor(max_workers=N_CHUNKS) as executor: # assuming you have 6 processors
the_futures = {}
start = 0
for i in range(N_CHUNKS - 1):
future = executor.submit(fnc, data, mesh[start:start+chunk_size]) # pass slice
the_futures[future] = (start, start+chunk_size) # map future to request parameters
start += chunk_size
if last_chunk_size:
future = executor.submit(fnc, data, mesh[start:n]) # pass slice
the_futures[future] = (start, start+n)
for future in as_completed(the_futures):
(start, end) = the_futures[future] # the original range
d = future.result() # do something with the results
if __name__ == '__main__':
# the call to main must be done in a block governed by if __name__ == '__main__' or you will get into a recursive
# loop where each subprocess calls main again
main(data, mesh)

How to use python multiprocessing Pool.map within loop

I am running a simulation using Runge-Kutta. At every time step two FFT of two independent variables are necessary which can be parallelized. I implemented the code like this:
from multiprocessing import Pool
import numpy as np
pool = Pool(processes=2) # I like to calculate only 2 FFTs parallel
# in every time step, therefor 2 processes
def Splitter(args):
'''I have to pass 2 arguments'''
return makeSomething(*args):
def makeSomething(a,b):
'''dummy function instead of the one with the FFT'''
return a*b
def RungeK():
# ...
# a lot of code which create the vectors A and B and calculates
# one Kunge-Kutta step for them
# ...
n = 20 # Just something for the example
A = np.arange(50000)
B = np.ones_like(A)
for i in xrange(n): # loop over the time steps
A *= np.mean(B)*B - A
B *= np.sqrt(A)
results = pool.map(Splitter,[(A,3),(B,2)])
A = results[0]
B = results[1]
print np.mean(A) # Some output
print np.max(B)
if __name__== '__main__':
RungeK()
Unfortunately python generates a unlimited number of processes after reaching the loop. Before it seems that only two processes are running. Also my memory fills up. Adding a
pool.close()
pool.join()
behind the loop does not solve my problem, and to put it inside the loop makes no sense for me. Hope you can help.

Move the creation of the pool into the RungeK function;
def RungeK():
# ...
# a lot of code which create the vectors A and B and calculates
# one Kunge-Kutta step for them
# ...
pool = Pool(processes=2)
n = 20 # Just something for the example
A = np.arange(50000)
B = np.ones_like(A)
for i in xrange(n): # loop over the time steps
A *= np.mean(B)*B - A
B *= np.sqrt(A)
results = pool.map(Splitter, [(A, 3), (B, 2)])
A = results[0]
B = results[1]
pool.close()
print np.mean(A) # Some output
print np.max(B)
Alternatively, put it in the main block.
This is probably a side effect of how multiprocessing works. E.g. on MS windows, you need to be able to import the main module without side effects (like creating new processes).

Multiprocessing in python to speed up functions

I am confused with Python multiprocessing.
I am trying to speed up a function which process strings from a database but I must have misunderstood how multiprocessing works because the function takes longer when given to a pool of workers than with “normal processing”.
Here an example of what I am trying to achieve.
from time import clock, time
from multiprocessing import Pool, freeze_support
from random import choice
def foo(x):
TupWerteMany = []
for i in range(0,len(x)):
TupWerte = []
s = list(x[i][3])
NewValue = choice(s)+choice(s)+choice(s)+choice(s)
TupWerte.append(NewValue)
TupWerte = tuple(TupWerte)
TupWerteMany.append(TupWerte)
return TupWerteMany
if __name__ == '__main__':
start_time = time()
List = [(u'1', u'aa', u'Jacob', u'Emily'),
(u'2', u'bb', u'Ethan', u'Kayla')]
List1 = List*1000000
# METHOD 1 : NORMAL (takes 20 seconds)
x2 = foo(List1)
print x2[1:3]
# METHOD 2 : APPLY_ASYNC (takes 28 seconds)
# pool = Pool(4)
# Werte = pool.apply_async(foo, args=(List1,))
# x2 = Werte.get()
# print '--------'
# print x2[1:3]
# print '--------'
# METHOD 3: MAP (!! DOES NOT WORK !!)
# pool = Pool(4)
# Werte = pool.map(foo, args=(List1,))
# x2 = Werte.get()
# print '--------'
# print x2[1:3]
# print '--------'
print 'Time Elaspse: ', time() - start_time
My questions:
Why does apply_async takes longer than the “normal way” ?
What I am doing wrong with map?
Does it makes sense to speed up such tasks with multiprocessing at all?
Finally: after all I have read here, I am wondering if multiprocessing in python works on windows at all ?

So your first problem is that there is no actual parallelism happening in foo(x), you are passing the entire list to the function once.
1)
The idea of a process pool is to have many processes doing computations on separate bits of some data.
# METHOD 2 : APPLY_ASYNC
jobs = 4
size = len(List1)
pool = Pool(4)
results = []
# split the list into 4 equally sized chunks and submit those to the pool
heads = range(size/jobs, size, size/jobs) + [size]
tails = range(0,size,size/jobs)
for tail,head in zip(tails, heads):
werte = pool.apply_async(foo, args=(List1[tail:head],))
results.append(werte)
pool.close()
pool.join() # wait for the pool to be done
for result in results:
werte = result.get() # get the return value from the sub jobs
This will only give you an actual speedup if the time it takes to process each chunk is greater than the time it takes to launch the process, in the case of four processes and four jobs to be done, of course these dynamics change if you've got 4 processes and 100 jobs to be done. Remember that you are creating a completely new python interpreter four times, this isn't free.
2) The problem you have with map is that it applies foo to EVERY element in List1 in a separate process, this will take quite a while. So if you're pool has 4 processes map will pop an item of the list four times and send it to a process to be dealt with - wait for process to finish - pop some more stuff of the list - wait for the process to finish. This makes sense only if processing a single item takes a long time, like for instance if every item is a file name pointing to a one gigabyte text file. But as it stands map will just take a single string of the list and pass it to foo where as apply_async takes a slice of the list. Try the following code
def foo(thing):
print thing
map(foo, ['a','b','c','d'])
That's the built-in python map and will run a single process, but the idea is exactly the same for the multiprocess version.
Added as per J.F.Sebastian's comment: You can however use the chunksize argument to map to specify an approximate size of for each chunk.
pool.map(foo, List1, chunksize=size/jobs)
I don't know though if there is a problem with map on Windows as I don't have one available for testing.
3) yes, given that your problem is big enough to justify forking out new python interpreters
4) can't give you a definitive answer on that as it depends on the number of cores/processors etc. but in general it should be fine on Windows.

On question (2)
With the guidance of Dougal and Matti, I figured out what's went wrong.
The original foo function processes a list of lists, while map requires a function to process single elements.
The new function should be
def foo2 (x):
TupWerte = []
s = list(x[3])
NewValue = choice(s)+choice(s)+choice(s)+choice(s)
TupWerte.append(NewValue)
TupWerte = tuple(TupWerte)
return TupWerte
and the block to call it :
jobs = 4
size = len(List1)
pool = Pool()
#Werte = pool.map(foo2, List1, chunksize=size/jobs)
Werte = pool.map(foo2, List1)
pool.close()
print Werte[1:3]
Thanks to all of you who helped me understand this.
Results of all methods:
for List * 2 Mio records: normal 13.3 seconds , parallel with async: 7.5 seconds, parallel with with map with chuncksize : 7.3, without chunksize 5.2 seconds

Here's a generic multiprocessing template if you are interested.
import multiprocessing as mp
import time
def worker(x):
time.sleep(0.2)
print "x= %s, x squared = %s" % (x, x*x)
return x*x
def apply_async():
pool = mp.Pool()
for i in range(100):
pool.apply_async(worker, args = (i, ))
pool.close()
pool.join()
if __name__ == '__main__':
apply_async()
And the output looks like this:
x= 0, x squared = 0
x= 1, x squared = 1
x= 2, x squared = 4
x= 3, x squared = 9
x= 4, x squared = 16
x= 6, x squared = 36
x= 5, x squared = 25
x= 7, x squared = 49
x= 8, x squared = 64
x= 10, x squared = 100
x= 11, x squared = 121
x= 9, x squared = 81
x= 12, x squared = 144
As you can see, the numbers are not in order, as they are being executed asynchronously.