[SciPy-User] help speeding up a Runge-Kuta algorithm (cython, f2py, ...)

[Ryan Krauss]

Thanks to Sturla for helping me get this working in Cython.

I am trying to compile the code to compare it against fortran for
speed.  I have run into two bugs so far (I mentioned that my C skills
are weak).

The first has to do with the "const trick":
Error compiling Cython file:
------------------------------------------------------------
...
cdef inline void dxdt_runge_kuta(double *x "const double *",
                                 double voltage "const double",
                                 double *dxdt):
    #cdef double J = 0.0011767297528720126 "const double"
    cdef double J = 0.0011767297528720126
    cdef double alpha0 = 4.1396263800000002 "const double"
                                                                 ^
------------------------------------------------------------

runge_kuta_v2.pyx:12:44: Syntax error in C variable declaration

I don't know what the problem is here, so for now I just got rid of
all the "const double" statements. (In case the formatting doesn't
come through, the little error carrot ^ points to the space between
the last number and the quote.

After getting rid of all the "const double" expressions (just to see
if everything else would compile), I got this:
Error compiling Cython file:
------------------------------------------------------------
...
    dxdt[0] = vel
    dxdt[1] = accel
    dxdt[2] = dzdt


def runge_kuta_one_step(double _x[::1], Py_ssize_t factor, double volts,
                                                  ^
------------------------------------------------------------

runge_kuta_v2.pyx:31:34: Expected an identifier or literal

The carrot points to the first square bracket.

Thanks,

Ryan


On Sat, Aug 4, 2012 at 6:28 PM, Sturla Molden <sturla at molden.no> wrote:
> Not tested and debugged, but to me it looks like something like this might
> be what you want.
>
> Sturla
>
>
> Den 03.08.2012 19:02, skrev Ryan Krauss:
>
> I need help speeding up some code I wrote to perform a Runge-Kuta
> integration.  I need to do the integration as part of a real-time
> control algorithm, so it needs to be fairly fast.
> scipy.integrate.odeint does too much error checking to be fast enough.
>  My pure Python version was just a little too slow, so I tried coding
> it up in Cython.  I have only used Cython once before, so I don't know
> if I did it correctly (the .pyx file is attached).
>
> The code runs just fine, but there is almost no speed up.  I think the
> core issue is that my dxdt_runge_kuta function gets called about 4000
> times per second, so most of my overhead is in the function calls (I
> think).  I am running my real-time control algorithm at 500 Hz and I
> need at least 2 Runge-Kuta integration steps per real-time steps for
> numeric stability.  And the Runge-Kuta algorithm needs to evaluate the
> derivative 4 times per times step.  So, 500 Hz * 2 * 4 = 4000 calls
> per second.
>
> I also tried coding this up in fortran and using f2py, but I am
> getting a type mismatch error I don't understand.  I have a function
> that declares its return values as double precision:
>
> double precision function dzdt(x,voltage)
>
> and I declare the variable I want to store the returned value in to
> also be double precision:
>
> double precision F,z,vel,accel,zdot1,zdot2,zdot3,zdot4
>
> zdot1 = dzdt(x_prev,volts)
>
> but some how it is not happy.
>
>
> My C skills are pretty weak (the longer I use Python, the more C I
> forget, and I didn't know that much to start with).  I started looking
> into Boost as well as using f2py on C code, but I got stuck.
>
>
> Can anyone either make my Cython or Fortran approaches work or point
> me in a different direction?
>
> Thanks,
>
> Ryan
>
>
>
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