Cython code 3-4 times slower than Python / Numpy code?
Question
I am trying to convert my Python / Numpy code to Cython code for speedup purposes. However, Cython is MUCH slower (3-4 times) than the Python / Numpy code. Am I using Cython correctly? Am I passing arguments correctly to myc_rb_etc() in my Cython code? What about when I call the integrate function? Thank you in advance for your help. Here is my Python / Numpy code:
from pylab import *
import pylab as pl
from numpy import *
import numpy as np
from scipy import integrate
def myc_rb_e2f(y,t,k,d):
M = y[0]
E = y[1]
CD = y[2]
CE = y[3]
R = y[4]
RP = y[5]
RE = y[6]
S = 0.01
if t > 300:
S = 5.0
#if t > 400
#S = 0.01
t1 = k[0]*S/(k[7]+S);
t2 = k[1]*(M/(k[14]+M))*(E/(k[15]+E));
t3 = k[5]*M/(k[14]+M);
t4 = k[11]*CD*RE/(k[16]+RE);
t5 = k[12]*CE*RE/(k[17]+RE);
t6 = k[2]*M/(k[14]+M);
t7 = k[3]*S/(k[7]+S);
t8 = k[6]*E/(k[15]+E);
t9 = k[13]*RP/(k[18]+RP);
t10 = k[9]*CD*R/(k[16]+R);
t11 = k[10]*CE*R/(k[17]+R);
dM = t1-d[0]*M
dE = t2+t3+t4+t5-k[8]*R*E-d[1]*E
dCD = t6+t7-d[2]*CD
dCE = t8-d[3]*CE
dR = k[4]+t9-k[8]*R*E-t10-t11-d[4]*R
dRP = t10+t11+t4+t5-t9-d[5]*RP
dRE = k[8]*R*E-t4-t5-d[6]*RE
dy = [dM,dE,dCD,dCE,dR,dRP,dRE]
return dy
t = np.zeros(10000)
t = np.linspace(0.,3000.,10000.)
# Initial concentrations of [M,E,CD,CE,R,RP,RE]
y0 = np.array([0.,0.,0.,0.,0.4,0.,0.25])
E_simulated = np.zeros([10000,5000])
E_avg = np.zeros([10000])
k = np.zeros([19])
d = np.zeros([7])
for i in range (0,5000):
k[0] = 1.+0.1*randn(1)
k[1] = 0.15+0.05*randn(1)
k[2] = 0.2+0.05*randn(1)
k[3] = 0.2+0.05*randn(1)
k[4] = 0.35+0.05*randn(1)
k[5] = 0.001+0.0001*randn(1)
k[6] = 0.5+0.05*randn(1)
k[7] = 0.3+0.05*randn(1)
k[8] = 30.+5.*randn(1)
k[9] = 18.+3.*randn(1)
k[10] = 18.+3.*randn(1)
k[11] = 18.+3.*randn(1)
k[12] = 18.+3.*randn(1)
k[13] = 3.6+0.5*randn(1)
k[14] = 0.15+0.05*randn(1)
k[15] = 0.15+0.05*randn(1)
k[16] = 0.92+0.1*randn(1)
k[17] = 0.92+0.1*randn(1)
k[18] = 0.01+0.001*randn(1)
d[0] = 0.7+0.05*randn(1)
d[1] = 0.25+0.025*randn(1)
d[2] = 1.5+0.05*randn(1)
d[3] = 1.5+0.05*randn(1)
d[4] = 0.06+0.01*randn(1)
d[5] = 0.06+0.01*randn(1)
d[6] = 0.03+0.005*randn(1)
r = integrate.odeint(myc_rb_e2f,y0,t,args=(k,d))
E_simulated[:,i] = r[:,1]
for i in range(0,10000):
E_avg[i] = sum(E_simulated[i,:])/5000.
pl.plot(t,E_avg,'-ro')
pl.show()
Here is the code converted into Cython:
cimport numpy as np
import numpy as np
from numpy import *
import pylab as pl
from pylab import *
from scipy import integrate
def myc_rb_e2f(y,t,k,d):
cdef double M = y[0]
cdef double E = y[1]
cdef double CD = y[2]
cdef double CE = y[3]
cdef double R = y[4]
cdef double RP = y[5]
cdef double RE = y[6]
cdef double S = 0.01
if t > 300.0:
S = 5.0
#if t > 400
#S = 0.01
cdef double t1 = k[0]*S/(k[7]+S)
cdef double t2 = k[1]*(M/(k[14]+M))*(E/(k[15]+E))
cdef double t3 = k[5]*M/(k[14]+M)
cdef double t4 = k[11]*CD*RE/(k[16]+RE)
cdef double t5 = k[12]*CE*RE/(k[17]+RE)
cdef double t6 = k[2]*M/(k[14]+M)
cdef double t7 = k[3]*S/(k[7]+S)
cdef double t8 = k[6]*E/(k[15]+E)
cdef double t9 = k[13]*RP/(k[18]+RP)
cdef double t10 = k[9]*CD*R/(k[16]+R)
cdef double t11 = k[10]*CE*R/(k[17]+R)
cdef double dM = t1-d[0]*M
cdef double dE = t2+t3+t4+t5-k[8]*R*E-d[1]*E
cdef double dCD = t6+t7-d[2]*CD
cdef double dCE = t8-d[3]*CE
cdef double dR = k[4]+t9-k[8]*R*E-t10-t11-d[4]*R
cdef double dRP = t10+t11+t4+t5-t9-d[5]*RP
cdef double dRE = k[8]*R*E-t4-t5-d[6]*RE
dy = [dM,dE,dCD,dCE,dR,dRP,dRE]
return dy
def main():
cdef np.ndarray[double,ndim=1] t = np.zeros(10000)
t = np.linspace(0.,3000.,10000.)
# Initial concentrations of [M,E,CD,CE,R,RP,RE]
cdef np.ndarray[double,ndim=1] y0 = np.array([0.,0.,0.,0.,0.4,0.,0.25])
cdef np.ndarray[double,ndim=2] E_simulated = np.zeros([10000,5000])
cdef np.ndarray[double,ndim=2] r = np.zeros([10000,7])
cdef np.ndarray[double,ndim=1] E_avg = np.zeros([10000])
cdef np.ndarray[double,ndim=1] k = np.zeros([19])
cdef np.ndarray[double,ndim=1] d = np.zeros([7])
cdef int i
for i in range (0,5000):
k[0] = 1.+0.1*randn(1)
k[1] = 0.15+0.05*randn(1)
k[2] = 0.2+0.05*randn(1)
k[3] = 0.2+0.05*randn(1)
k[4] = 0.35+0.05*randn(1)
k[5] = 0.001+0.0001*randn(1)
k[6] = 0.5+0.05*randn(1)
k[7] = 0.3+0.05*randn(1)
k[8] = 30.+5.*randn(1)
k[9] = 18.+3.*randn(1)
k[10] = 18.+3.*randn(1)
k[11] = 18.+3.*randn(1)
k[12] = 18.+3.*randn(1)
k[13] = 3.6+0.5*randn(1)
k[14] = 0.15+0.05*randn(1)
k[15] = 0.15+0.05*randn(1)
k[16] = 0.92+0.1*randn(1)
k[17] = 0.92+0.1*randn(1)
k[18] = 0.01+0.001*randn(1)
d[0] = 0.7+0.05*randn(1)
d[1] = 0.25+0.025*randn(1)
d[2] = 1.5+0.05*randn(1)
d[3] = 1.5+0.05*randn(1)
d[4] = 0.06+0.01*randn(1)
d[5] = 0.06+0.01*randn(1)
d[6] = 0.03+0.005*randn(1)
r = integrate.odeint(myc_rb_e2f,y0,t,args=(k,d))
E_simulated[:,i] = r[:,1]
for i in range(0,10000):
E_avg[i] = sum(E_simulated[i,:])/5000.
pl.plot(t,E_avg,'-ro')
pl.show()
Here are some pstats from cProfile on my Python / Numpy code:
ncalls tottime percall cumtime percall
5000 82.505 0.017 236.760 0.047 {scipy.integrate._odepack.odeint}
1 1.504 1.504 238.949 238.949 myc_rb_e2f.py:1(<module>)
5000 0.025 0.000 236.855 0.047 C:\\Python27\\lib\\site-packages\\scipy\\integrate\\odepack.py:18(odeint)
12291237 154.255 0.000 154.255 0.000 myc_rb_e2f.py:7(myc_rb_e2f)
2 answers
solution1
10 ACCPTED 2012-10-24 08:18:53
Change the function definition to include the types of the parameters:
def myc_rb_e2f(np.ndarray[double,ndim=1]y, double t, np.ndarray[double, ndim=1] k, np.ndarray[double, ndim=1] d):
This will improve the running time about 3 times over the numpy implementation and 6 - 7 times over your initial cython implementation. Just FYI, I lowered the number of iterations so that I didn't have to wait forever for it to finish while testing. It should scale up with your desired number of iterations.
[pkerp@plastilin so]$ time python run_numpy.py
real 0m47.572s
user 0m45.702s
sys 0m0.049s
[pkerp@plastilin so]$ time python run_cython1.py
real 1m14.851s
user 1m12.308s
sys 0m0.135s
[pkerp@plastilin so]$ time python run_cython2.py
real 0m15.774s
user 0m14.115s
sys 0m0.105s
Edit:
Also, you don't have to create a new array each time you want to return the results from myc_rb_e2f . You can just declare a results array in main , pass it in on every call, and then fill it in. It will save you a lot of unnecessary allocation. This more than halves the previous best running time:
[pkerp@plastilin so]$ time python run_cython3.py
real 0m6.165s
user 0m4.818s
sys 0m0.152s
And the code:
cimport numpy as np
import numpy as np
from numpy import *
import pylab as pl
from pylab import *
from scipy import integrate
def myc_rb_e2f(np.ndarray[double,ndim=1]y, double t, np.ndarray[double, ndim=1] k, np.ndarray[double, ndim=1] d, np.ndarray[double, ndim=1] res):
cdef double S = 0.01
if t > 300.0:
S = 5.0
#if t > 400
#S = 0.01
cdef double t1 = k[0]*S/(k[7]+S)
cdef double t2 = k[1]*(y[0]/(k[14]+y[0]))*(y[1]/(k[15]+y[1]))
cdef double t3 = k[5]*y[0]/(k[14]+y[0])
cdef double t4 = k[11]*y[2]*y[6]/(k[16]+y[6])
cdef double t5 = k[12]*y[3]*y[6]/(k[17]+y[6])
cdef double t6 = k[2]*y[0]/(k[14]+y[0])
cdef double t7 = k[3]*S/(k[7]+S)
cdef double t8 = k[6]*y[1]/(k[15]+y[1])
cdef double t9 = k[13]*y[5]/(k[18]+y[5])
cdef double t10 = k[9]*y[2]*y[4]/(k[16]+y[4])
cdef double t11 = k[10]*y[3]*y[4]/(k[17]+y[4])
cdef double dM = t1-d[0]*y[0]
cdef double dE = t2+t3+t4+t5-k[8]*y[4]*y[1]-d[1]*y[1]
cdef double dCD = t6+t7-d[2]*y[2]
cdef double dCE = t8-d[3]*y[3]
cdef double dR = k[4]+t9-k[8]*y[4]*y[1]-t10-t11-d[4]*y[4]
cdef double dRP = t10+t11+t4+t5-t9-d[5]*y[5]
cdef double dRE = k[8]*y[4]*y[1]-t4-t5-d[6]*y[6]
res[0] = dM
res[1] = dE
res[2] = dCD
res[3] = dCE
res[4] = dR
res[5] = dRP
res[6] = dRE
return res
def main():
cdef np.ndarray[double,ndim=1] t = np.zeros(467)
cdef np.ndarray[double,ndim=1] results = np.zeros(7)
t = np.linspace(0.,3000.,467.)
# Initial concentrations of [M,E,CD,CE,R,RP,RE]
cdef np.ndarray[double,ndim=1] y0 = np.array([0.,0.,0.,0.,0.4,0.,0.25])
cdef np.ndarray[double,ndim=2] E_simulated = np.zeros([467,554])
cdef np.ndarray[double,ndim=2] r = np.zeros([467,7])
cdef np.ndarray[double,ndim=1] E_avg = np.zeros([467])
cdef np.ndarray[double,ndim=1] k = np.zeros([19])
cdef np.ndarray[double,ndim=1] d = np.zeros([7])
cdef int i
for i in range (0,554):
k[0] = 1.+0.1*randn(1)
k[1] = 0.15+0.05*randn(1)
k[2] = 0.2+0.05*randn(1)
k[3] = 0.2+0.05*randn(1)
k[4] = 0.35+0.05*randn(1)
k[5] = 0.001+0.0001*randn(1)
k[6] = 0.5+0.05*randn(1)
k[7] = 0.3+0.05*randn(1)
k[8] = 30.+5.*randn(1)
k[9] = 18.+3.*randn(1)
k[10] = 18.+3.*randn(1)
k[11] = 18.+3.*randn(1)
k[12] = 18.+3.*randn(1)
k[13] = 3.6+0.5*randn(1)
k[14] = 0.15+0.05*randn(1)
k[15] = 0.15+0.05*randn(1)
k[16] = 0.92+0.1*randn(1)
k[17] = 0.92+0.1*randn(1)
k[18] = 0.01+0.001*randn(1)
d[0] = 0.7+0.05*randn(1)
d[1] = 0.25+0.025*randn(1)
d[2] = 1.5+0.05*randn(1)
d[3] = 1.5+0.05*randn(1)
d[4] = 0.06+0.01*randn(1)
d[5] = 0.06+0.01*randn(1)
d[6] = 0.03+0.005*randn(1)
r = integrate.odeint(myc_rb_e2f,y0,t,args=(k,d,results))
E_simulated[:,i] = r[:,1]
for i in range(0,467):
E_avg[i] = sum(E_simulated[i,:])/554.
#pl.plot(t,E_avg,'-ro')
#pl.show()
if __name__ == "__main__":
main()
solution2
1 2017-01-04 08:31:57
disclaimer: I'm one of the core dev of the sub-mentioned tool
As an alternative, extracting the myc_rb_e2f function to a single file and compiling it with pythran yields a x5 speedup on my side.
The extracted python code (only a type signature) added:
#pythran export myc_rb_e2f(float[], float, float[], float[])
def myc_rb_e2f(y,t,k,d):
M = y[0]
E = y[1]
CD = y[2]
CE = y[3]
R = y[4]
RP = y[5]
RE = y[6]
S = 0.01
if t > 300:
S = 5.0
#if t > 400
#S = 0.01
t1 = k[0]*S/(k[7]+S);
t2 = k[1]*(M/(k[14]+M))*(E/(k[15]+E));
t3 = k[5]*M/(k[14]+M);
t4 = k[11]*CD*RE/(k[16]+RE);
t5 = k[12]*CE*RE/(k[17]+RE);
t6 = k[2]*M/(k[14]+M);
t7 = k[3]*S/(k[7]+S);
t8 = k[6]*E/(k[15]+E);
t9 = k[13]*RP/(k[18]+RP);
t10 = k[9]*CD*R/(k[16]+R);
t11 = k[10]*CE*R/(k[17]+R);
dM = t1-d[0]*M
dE = t2+t3+t4+t5-k[8]*R*E-d[1]*E
dCD = t6+t7-d[2]*CD
dCE = t8-d[3]*CE
dR = k[4]+t9-k[8]*R*E-t10-t11-d[4]*R
dRP = t10+t11+t4+t5-t9-d[5]*RP
dRE = k[8]*R*E-t4-t5-d[6]*RE
dy = [dM,dE,dCD,dCE,dR,dRP,dRE]
return dy
compiled with:
> pythran myc.py
And imported with from myc import myc_rb_e2f in place of the function definition.
The original code runs in 103.46s on my laptop, the one calling the pythran-compiled routine runs in 22.23s .
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