Distributing tasks on several cores with IPython.parallel



In [1]:

    
import numpy as np
from ipyparallel import Client  # IPython.parallel before IPython 4.0



In [2]:

    
rc = Client()

Direct interface



In [3]:

    
rc.ids









    Out[3]:





[0, 1, 2, 3]



In [4]:

    
%px import os, time



In [5]:

    
%px print(os.getpid())









    Out[5]:





[stdout:0] 11173
[stdout:1] 11174
[stdout:2] 11175
[stdout:3] 11176



In [6]:

    
%%px --targets :-1
print(os.getpid())









    Out[6]:





[stdout:0] 11173
[stdout:1] 11174
[stdout:2] 11175



In [7]:

    
view = rc[:-1]
view









    Out[7]:





<DirectView [0, 1, 2]>

Load-balanced interface



In [8]:

    
v = rc.load_balanced_view()



In [9]:

    
def sample(n):
    import numpy as np
    # Random coordinates.
    x, y = np.random.rand(2, n)
    # Square distances to the origin.
    r_square = x ** 2 + y ** 2
    # Number of points in the quarter disc.
    return (r_square <= 1).sum()



In [10]:

    
def pi(n_in, n):
    return 4. * float(n_in) / n



In [11]:

    
n = 100000000



In [12]:

    
pi(sample(n), n)









    Out[12]:





3.14174968



In [13]:

    
%timeit pi(sample(n), n)









    Out[13]:





1 loops, best of 3: 2.65 s per loop



In [14]:

    
args = [n // 100] * 100



In [15]:

    
ar = v.map(sample, args)



In [16]:

    
ar.ready(), ar.progress









    Out[16]:





(False, 12)



In [17]:

    
ar.elapsed, ar.serial_time









    Out[17]:





(1.428284, 4.042367000000002)



In [18]:

    
pi(np.sum(ar.result), n)









    Out[18]:





3.141666