On 3/7/07, Daniel Mahler <[EMAIL PROTECTED]> wrote:
My problem is not space, but time.
I am creating a small array over and over,
and this is turning out to be a bottleneck.
My experiments suggest that problem is the allocation,
not the random number generation.
Allocating all the arrays as one n+1 dim and grabbing rows from it
is faster than allocating the small arrays individually.
I am iterating too many times to allocate everything at once though.
I can just do a nested loop
where create manageably large arrays in the outer
and grab the rows in the inner,
but I wanted something cleaner.
Besides, I thought avoiding allocation altogether would be even faster.
The slow down is probably related to this from a previous thread:
In [46]: def test1() :
....: x = normal(0,1,1000)
....:
In [47]: def test2() :
....: for i in range(1000) :
....: x = normal(0,1)
In [50]: t = timeit.Timer('test1()', "from __main__ import test1, normal")
In [51]: t.timeit(100)
Out[51]: 0.022681951522827148
In [52]: t = timeit.Timer('test2()', "from __main__ import test2, normal")
In [53]: t.timeit(100)
Out[53]: 4.3481810092926025
Robert thought this might relate to Travis' changes adding broadcasting to
the random number generator. It does seem certain that generating small
arrays of random numbers has a very high overhead.
Chuck
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