[SciPy-dev] Progress with linalg2

[eric]

Hey Pearu,

3 things:

1.  My atlas is missing some routines that you have in generic_clapack.pyf.  
    Specifically:

        clapack_xgetri
        clapack_xpotri
        clapack_xlauum
        clapack_xtrtri

    where x stands for the various types.  Are these in your ATLAS?  I have a
    fairly recent version, but perhaps I need to upgrade?

2.  I get segfaults when trying to run the tests.  It looks like they happen
    for both C and Fortran (I commented out contiguous cases to test Fortran).
    Do you think this is a windows specific issue?  I am using the latest
    f2py CVS.

3.  I like your _measure tests.  I've long been wishing we had some set of
    benchmarks that we could test with something like:

       >>> import scipy
       >>> scipy.benchmark()

    This is a nice step in that direction.

eric


----- Original Message ----- 
From: "Pearu Peterson" <pearu at cens.ioc.ee>
To: <scipy-dev at scipy.org>
Sent: Saturday, March 02, 2002 12:28 PM
Subject: [SciPy-dev] Progress with linalg2


> 
> Hi,
> 
> 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)
> 
> Notes:
> 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
>    ATLAS-3.3.13.
> 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.
> 
> Conclusions:
> 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):
> 
>   cd linalg2
>   python setup_linalg.py build --build-platlib=.
>   python tests/test_basic.py
> 
> Regards,
> Pearu
> 
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