multiprocessing

1"""A very simple parallel code example to execute parallel functions in python"""
2import multiprocessing
3import numpy as np
4def multiprocessing_func(x):
5	"""Individually prints the squares y_i of the elements x_i of a vector x"""
6	for x_i in x:
7		y=x_i**2 
8		print('The square of ',x_i,' is ',y)
9def chunks(input, n):
10    """Yields successive n-sized chunks of input"""
11    for i in range(0, len(input), n):
12        yield input[i:i + n]
13if __name__=='__main__':
14	n_proc=4 #Numer of available processors
15	x=np.arange(100) #Input
16	chunked_x=list(chunks(x, int(x.shape[0]/n_proc)+1)) #Splits input among n_proc chunks
17	processes=[] #Initialize the parallel processes list
18	for i in np.arange(0,n_proc):
19		"""Execute the target function on the n_proc target processors using the splitted input""" 
20		p = multiprocessing.Process(target=multiprocessing_func,args=(chunked_x[i],))
21		processes.append(p)
22		p.start()
23	for process in processes:
24		process.join()

1from multiprocessing import Process
2
3def say_hello(name='world'):
4    print "Hello, %s" % name
5
6p = Process(target=say_hello)
7p.start()
8p.join()	# Tells the program to wait until p has finished it's job before exiting
9

1# Parallelizing using Pool.apply()
2
3import multiprocessing as mp
4
5# Step 1: Init multiprocessing.Pool()
6pool = mp.Pool(mp.cpu_count())
7
8# Step 2: `pool.apply` the `howmany_within_range()`
9results = [pool.apply(howmany_within_range, args=(row, 4, 8)) for row in data]
10
11# Step 3: Don't forget to close
12pool.close()    
13
14print(results[:10])
15#> [3, 1, 4, 4, 4, 2, 1, 1, 3, 3]