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Numpy Bench

import numpy as np import time import math bt = time.time() n = 1000 for i in range(1): a = np.random.uniform(low=0., high=1., size=(n, n)).astype(np.float32) b = np.random.uniform(low=0., high=1., size=(n, n)).astype(np.float32) a = a.dot(b) print ('(%d * %d),Total Time:%.3f'%(n,n,time.time()bt)) start = time.clock() x = [i * 0.001 for i in xrange(10000000)] for i, t in enumerate(x): x[i] = math.sin(t) print ("math.sin:%.3f"%(time.clock()  start)) start = time.clock() y = [i * 0.001 for i in xrange(10000000)] y = np.array(y) print ("numpy.sin1:%.3f"%(time.clock()  start)) start = time.clock() np.sin(y,y) print ("numpy.sin2:%.3f"%(time.clock()  start)) ''' * ipad Pro 9.7 (1000 * 1000),Total Time:8.840 math.sin: 3.04883 numpy.sin: 2.668903 (2.517,0.177) =====Android Termux===== (1000,1000) 0.627 math.sin: 33.899 numpy.sin: 14.953 =====MacBook Air 13 @2010==== (1000,1000) 0.234 math.sin: 8.95 numpy.sin: 4.71 (3.88,0.48) ====Microsoft Surface 3 @2015==== (1000,1000) 0.203 math.sin: 12.739 numpy.sin: (5.419,0.380)
Why iOS numpy (1000,1000) very slow ???

is this the same exact code run on all devices? clearly not, since the output is different
Note the time here is the time to run random.uniform(1000,1000) twice, plus the dot product. The dot product is the time consuming bit.

all devices run same code.

it is not the same code, since the output is different form. maybe you use * instead of .dot() for the other platfoms?
Alternatively, perhaps the matrix multiplication is not well optimized on ios (i.e using all of the BLAS or Accelerate, etc)

I got the below from my iPad Pro 12.9' latest model and the latest beta. I ran it under 2.7 as I guess thats the target because of the use of xrange.
(1000 * 1000),Total Time:5.750
math.sin:2.757
numpy.sin1:2.156
numpy.sin2:0.131 
iPhone X (Pythonista V3.2 , Python 2.7)
(1000 * 1000),Total Time:6.435
math.sin:2.250
numpy.sin1:1.697
numpy.sin2:0.111 
as an aside  you shouls never create arrays like this... use np.linspace! np.linspace(0,10,1000000) is about 10x faster than list comprehension!