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POWhy does iterative elementwise array multiplication slow down in numpy?
 

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  1. POWhy does iterative elementwise array multiplication slow down in numpy?
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    <p>The code below reproduces the problem I have encountered in the algorithm I'm currently implementing:</p> <pre><code>import numpy.random as rand import time x = rand.normal(size=(300,50000)) y = rand.normal(size=(300,50000)) for i in range(1000): t0 = time.time() y *= x print "%.4f" % (time.time()-t0) y /= y.max() #to prevent overflows </code></pre> <p>The problem is that after some number of iterations, things start to get gradually slower until one iteration takes multiple times more time than initially.</p> <p>A plot of the slowdown <img src="https://i.stack.imgur.com/S839J.png" alt="enter image description here"></p> <p>CPU usage by the Python process is stable around 17-18% the whole time.</p> <p>I'm using: </p> <ul> <li>Python 2.7.4 32-bit version;</li> <li>Numpy 1.7.1 with MKL;</li> <li>Windows 8.</li> </ul>
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    1. COI don't think I see this behavior with python-2.7.4 under Linux.
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      1. POWhy does iterative elementwise array multiplication slow down in numpy?
      1. USBenjamin Bannier
    2. COIt's probably due to denormal numbers: http://stackoverflow.com/a/9314926/226621
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      1. POWhy does iterative elementwise array multiplication slow down in numpy?
      1. USAlok Singhal
    3. COIn my test run, as soon as it started slowing down, I interrupted it and did `print numpy.amin(numpy.abs(y[y != 0]))` and got `4.9406564584124654e-324`, so I think denormal numbers are your answer. I don't know how to flush denormals to zero from within Python other than creating a C extension though...
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      1. POWhy does iterative elementwise array multiplication slow down in numpy?
      1. USAlok Singhal
 

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