Hi all!
I have a large collection (~50,000) of integer vectors in low dimension
(~20). For each of these vectors, I want to divide all entries by their
gcd. In other words if
def prim_v(v):
d = abs(gcd(v))
return v.apply_map(lambda vi: vi.divide_knowing_divisible_by(d))
then I want to compute map(prim_v, V) for a long list V. When trying to
profile this using %prun, I found it difficult to tell which part of this
computation is taking the most time. So I rewrote this as
def foo(v,d):
return v.divide_knowing_divisible_by(d)
def bar(v):
return abs(gcd(v))
def prim_v(v):
d = bar(v)
return v.apply_map(lambda vi: foo(vi,d))
Then, profiling this with %prun yields the following information.
ncalls tottime percall cumtime percall filename:lineno(function)
47802 0.119 0.000 13.811 0.000 LDsolver.py:58(prim_v)
47802 0.116 0.000 3.593 0.000 LDsolver.py:54(bar)
859898 0.360 0.000 0.524 0.000 LDsolver.py:51(foo)
If I am reading this correctly, this means that most of the time (~10
seconds) is not spent doing the actual computation (using foo and bar) but
simply creating vectors and read/writing values to/from vectors (in
apply_map). (Do something like return vector((foo(vi,d) for vi in v)) instead
of apply_map does not make much a difference.)
Now, my questions are:
1) Why is this so slow? Am I missing something here?
2) Is there anything I can do to improve performance?
Thank you very much,
Felix
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