Wed, 21 Oct 2009 16:48:22 +0900, Mathieu Blondel wrote:
[clip]
> My original idea was to write the code in C with Intel/Alvitec/Neon
> intrinsics and have this code binded to be able to call it from Python.
> So the SIMD code would be compiled already, ready to be called from
> Python. Like you said, there's a risk that the overhead of calling
> Python is bigger than the benefit of using SIMD instructions. If it's
> worth trying out, an experiment can be made with Vector4f to see if it's
> even worth continuing with other types.
The overhead is quickly checked for multiplication with numpy arrays of
varying size, without SSE:
Overhead per iteration (ms): 1.6264549101
Time per array element (ms): 0.000936947636565
Cross-over point: 1735.90801303
#----------------------------------------------
import numpy as np
from scipy import optimize
import time
import matplotlib.pyplot as plt
def main():
data = []
for n in np.unique(np.logspace(0, 5, 20).astype(int)):
print n
m = 100
reps = 5
times = []
for rep in xrange(reps):
x = np.zeros((n,), dtype=np.float_)
start = time.time()
#------------------
for k in xrange(m):
x *= 1.1
#------------------
end = time.time()
times.append(end - start)
t = min(times)
data.append((n, t))
data = np.array(data)
def model(z):
n, t = data.T
overhead, per_elem = z
return np.log10(t) - np.log10(overhead + per_elem * n)
z, ier = optimize.leastsq(model, [1., 1.])
overhead, per_elem = z
print ""
print "Overhead per iteration (ms):", overhead*1e3
print "Time per array element (ms):", per_elem*1e3
print "Cross-over point: ", overhead/per_elem
n = np.logspace(0, 5, 500)
plt.loglog(data[:,0], data[:,0]/data[:,1], 'x',
label=r'measured')
plt.loglog(n, n/(overhead + per_elem*n), 'k-',
label=r'fit to $t = a + b n$')
plt.xlabel(r'$n$')
plt.ylabel(r'ops/second')
plt.grid(1)
plt.legend()
plt.show()
if __name__ == "__main__":
main()
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