[SciPy-User] help speeding up a Runge-Kuta algorithm (cython, f2py, ...)
Sturla Molden
sturla@molden...
Sat Aug 4 18:28:48 CDT 2012
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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#cython: boundscheck=False
#cython: wraparound=False
import numpy as np
from libc.math cimport exp, fabs
cdef inline void dxdt_runge_kuta(double *x "const double *",
double voltage "const double",
double *dxdt):
cdef double J = 0.0011767297528720126 "const double" # these are all constants, using the "const trick"
cdef double alpha0 = 4.1396263800000002 "const double"
cdef double alpha1 = 8.7203167700000002 "const double"
cdef double alpha2 = 0.010979707700000001 "const double"
cdef double sigma0 = 2.391621670693391 "const double"
cdef double sigma1 = 0.005368467489711875 "const double"
cdef double v_gain = 1.3319218899042409 "const double"
cdef double v_stribeck = 655.33634300000006 "const double"
cdef double vel = x[1]
cdef double z = x[2]
cdef double g = alpha0 + alpha1*exp(-1*(vel/v_stribeck)**2)
cdef double dzdt = vel - sigma0*z*fabs(vel)/g
cdef double F = sigma0*z + sigma1*dzdt + alpha2*vel
cdef double accel = 1.0/J*(voltage*v_gain - F)
dxdt[0] = vel
dxdt[1] = accel
dxdt[2] = dzdt
def runge_kuta_one_step(double _x[::1], Py_ssize_t factor, double volts,
double dt_rk):
cdef double g1[4], g2[4], g3[4], g4[4], dxdt[4], tmpx[4] # padding with one element for better alignment
cdef double x[4]
cdef double out[::1] = np.zeros(3)
cdef Py_ssize_t q
cdef int i
cdef double *x_prev, *x_out, *swap
x_out = &out[0]
x_prev = &x[0]
x[0] = _x[0]
x[1] = _x[1]
x[2] = _x[2]
for q in range(factor):
dxdt_runge_kuta(x_prev, volts, dxdt)
for i in range(3):
g1[i] = dt_rk * dxdt[i]
tmpx[i] = x_prev[i] + 0.5*g1[i]
dxdt_runge_kuta(tmpx, volts, dxdt)
for i in range(3):
g2[i] = dt_rk * dxdt[i]
tmpx[i] = x_prev[i] + 0.5*g2[i]
dxdt_runge_kuta(tmpx, volts, dxdt)
for i in range(3):
g3[i] = dt_rk * dxdt[i]
tmpx[i] = x_prev[i] + g3[i]
dxdt_runge_kuta(tmpx, volts, dxdt)
for i in range(3):
g4[i] = dt_rk * dxdt[i]
for i in range(3):
x_out[i] = x_prev[i] + 1.0/6*(g1[i] + 2*g2[i] + 2*g3[i] + g4[i])
swap = x_prev
x_prev = x_out
x_out = swap
for i in range(3):
out[i] = x_prev[i]
return out
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