Hello Sameer,
Thank you very much for your reply. My goal was to try to speed up the loop
describing the accumulator. In the (excellent) book I was mentioning in my
initial post I could find one example that seemed to match what I was trying
to do. Basically, it is said that a loop of the following kind:
n = size(u)-1
for i in xrange(1,n,1):
u_new[i] = u[i-1] + u[i] + u[i+1]
should be equivalent to:
u[1:n] = u[0:n-1] + u[1:n] + u[i+1]
Am I missing something?
Regards,
Francesco
Sameer Grover wrote:
>
> On Saturday 07 April 2012 12:14 AM, francesco82 wrote:
>> Hello everyone,
>>
>> After reading the very good post
>> http://technicaldiscovery.blogspot.com/2011/06/speeding-up-python-numpy-cython-and.html
>> and the book by H. P. Langtangen 'Python scripting for computational
>> science' I was trying to speed up the execution of a loop on numpy arrays
>> being used to describe a simple difference equation.
>>
>> The actual code I am working on involves some more complicated equations,
>> but I am having the same exact behavior as described below. To test the
>> improvement in speed I wrote the following in vect_try.py:
>>
>> #!/usr/bin/python
>> import numpy as np
>> import matplotlib.pyplot as plt
>>
>> dt = 0.02 #time step
>> time = np.arange(0,2,dt) #time array
>> u = np.sin(2*np.pi*time) #input signal array
>>
>> def vect_int(u,y): #vectorized function
>> n = u.size
>> y[1:n] = y[0:n-1] + u[1:n]
>> return y
>>
>> def sc_int(u,y): #scalar function
>> y = y + u
>> return y
>>
>> def calc_vect(u, func=vect_int):
>> out = np.zeros(u.size)
>> for i in xrange(u.size):
>> out = func(u,out)
>> return out
>>
>> def calc_sc(u, func=sc_int):
>> out = np.zeros(u.size)
>> for i in xrange(u.size-1):
>> out[i+1] = sc_int(u[i+1],out[i])
>> return out
>>
>> To verify the execution time I've used the timeit function in Ipython:
>>
>> import vect_try as vt
>> timeit vt.calc_vect(vt.u) --> 1000 loops, best of 3: 494 us per loop
>> timeit vt.calc_sc(vt.u) -->10000 loops, best of 3: 92.8 us per loop
>>
>> As you can see the scalar implementation looping one item at the time
>> (calc_sc) is 494/92.8~5.3 times faster than the vectorized one
>> (calc_vect).
>>
>> My problem consists in the fact that I need to iterate the execution of
>> calc_vect in order for it to operate on all the elements of the input
>> array.
>> If I execute calc_vect only once, it will only operate on the first slice
>> of
>> the vectors leaving the remaining untouched. My understanding was that
>> the
>> vector expression y[1:n] = y[0:n-1] + u[1:n] was supposed to iterate over
>> all the array, but that's not happening for me. Can anyone tell me what I
>> am
>> doing wrong?
>>
>> Thanks!
>> Francesco
>>
> 1. By vectorizing, we mean replacing a loop with a single expression. In
> your program, both the scalar and vector implementations (calc_vect and
> calc_sc) have a loop each. This isn't going to make anything faster. The
> vectorized implementation is just a convoluted way of achieving the same
> result and is slower.
>
> 2. The expression y[1:n] = y[0:n-1] + u[1:n] is /not/ equivalent to the
> following loop
>
> for i in range(0,n-1):
> y[i+1] = y[i] + u[i+1]
>
> It is equivalent to something like
>
> z = np.zeros(n-1)
> for i in range(0,n-1):
> z[i] = y[i] + u[i+1]
> y[1:n] = z
>
> i.e., the RHS is computed in totality and then assigned to the LHS. This
> is how array operations work even in other languages such as Matlab.
>
> 3. I personally don't think there is a simple/obvious way to vectorize
> what you're trying to achieve.
>
> Sameer
>
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>
>
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