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On 8/3/2012 11:02 AM, Ryan Krauss wrote:
<pre wrap="">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
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.</pre>
I'm not much of a Fortran programmer and I may misunderstand the
above, but have you tried adding dzdt to your double precision
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?
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