I'm the main programmer for the PyQuante package, a quantum chemistry
package in Python. I'm trying to speed up one of my rate determining
steps. Essentially, I have to decide between two algorithms:
1. Packed means that I compute N**4/8 integrals, and then do a bunch
of indexing operations to unpack;
2. Unpacked means that I compute all N**4 integrals, but don't have to
do any indexing.
Raw timing the two options show that packed is clearly faster (12.5
sec vs 20.6 sec). However, the profilings show very different results.
I have the results below. Clearly I'm going to use the packed scheme.
My question to the mailing list is what am I doing wrong with my
profiling that it shows such poor predictions? I rely on profiling a
great deal to tune my algorithms, and I'm used to seeing differences,
but nothing close to this magnitude.
Here is packed:
ncalls tottime percall cumtime percall filename:lineno(function)
11021725 84.493 0.000 84.493 0.000 :0(ijkl2intindex)
18 62.064 3.448 119.865 6.659 Ints.py:150(getK)
18 32.063 1.781 61.186 3.399 Ints.py:131(getJ)
52975 9.404 0.000 19.658 0.000 CGBF.py:189(coulomb)
313643 2.542 0.000 2.542 0.000 :0(range)
52975 2.260 0.000 2.260 0.000 :0(contr_coulomb)
218200 1.377 0.000 1.377 0.000 CGBF.py:51(norm)
211900 1.337 0.000 1.337 0.000 CGBF.py:53(powers)
211900 1.336 0.000 1.336 0.000 CGBF.py:56(exps)
211900 1.329 0.000 1.329 0.000 CGBF.py:58(pnorms)
211900 1.328 0.000 1.328 0.000 CGBF.py:52(origin)
211900 1.328 0.000 1.328 0.000 CGBF.py:57(coefs)
1 0.979 0.979 21.108 21.108 Ints.py:112(get2ints)
11790 0.197 0.000 0.197 0.000 :0(dot)
11828 0.166 0.000 0.166 0.000 :0(zeros)
Here is unpacked:
ncalls tottime percall cumtime percall filename:lineno(function)
18 16.158 0.898 17.544 0.975 Ints.py:167(getK)
52975 9.301 0.000 19.515 0.000 CGBF.py:189(coulomb)
18 4.584 0.255 5.904 0.328 Ints.py:146(getJ)
313643 2.630 0.000 2.630 0.000 :0(range)
52975 2.254 0.000 2.254 0.000 :0(contr_coulomb)
218200 1.375 0.000 1.375 0.000 CGBF.py:51(norm)
211900 1.330 0.000 1.330 0.000 CGBF.py:58(pnorms)
211900 1.325 0.000 1.325 0.000 CGBF.py:53(powers)
211900 1.325 0.000 1.325 0.000 CGBF.py:57(coefs)
211900 1.323 0.000 1.323 0.000 CGBF.py:56(exps)
211900 1.321 0.000 1.321 0.000 CGBF.py:52(origin)
1 0.782 0.782 20.373 20.373 Ints.py:114(get2ints)
1875 0.156 0.000 0.384 0.000 CGBF.py:106(nuclear)
11790 0.147 0.000 0.147 0.000 :0(dot)
17856 0.112 0.000 0.112 0.000 PGBF.py:63(coef)
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