[SciPy-dev] Progress with linalg2
pearu at cens.ioc.ee
Sat Mar 2 11:28:26 CST 2002
I am working again with linalg2 and I have made some progress with it.
I have almost finished testing solve() function, other functions will
get out faster hopefully.
Here are some timing results that compare the corresponding functions of
scipy and Numeric:
Solving system of linear equations
| continuous | non-continuous
size | scipy | Numeric | scipy | Numeric
20 | 1.11 | 1.70 | 1.10 | 1.85 (secs for 2000 calls)
100 | 1.65 | 3.02 | 1.68 | 4.47 (secs for 300 calls)
500 | 1.73 | 2.14 | 1.78 | 2.33 (secs for 4 calls)
1000 | 5.60 | 6.23 | 5.59 | 7.03 (secs for 2 calls)
1) `Numeric' refers to using LinearAlgebra.solve_linear_equations().
2) `scipy' refers to using scipy.linalg.solve().
3) `size' is the number of equations.
4) Both continuous and non-continuous arrays were used in the tests.
5) Both Numeric and scipy use the same LAPACK routine dgesv from
6) The tests were run on PII-400MHz, 160MB RAM, Debian Woody
with gcc-2.95.4, Python 2.2, Numeric 20.3, f2py-2.13.175-1218.
1) The corresponding Scipy function is faster in all tests.
The difference gets smaller for larger tasks but it does not vanish.
2) Since both Scipy and Numeric functions use the same LAPACK
routine, then these tests actually measure the efficency of the interfaces
to LAPACK routines. In the Scipy case the interfaces are generated by
f2py and in the Numeric case by a man. These results show that it makes
sense to use automatically generated extension modules: one can always
tune the code generator for producing a better code while hand-written
extension modules will hardly get tuned in practice.
3) Note that there is almost no difference whether the input array to f2py
generated extension module is contiguous or non-contiguous, these
cases are efficently handled by the interface. While using the Numeric
interface, the difference is quite noticable.
Note also that in order to run these tests, one has to have
f2py-2.13.175-1218 (in f2py CVS) or later because earlier versions of f2py
leak memory. Here is how I run the tests (remember to cvs update):
python setup_linalg.py build --build-platlib=.
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