Optimizing a Function with Cython, Complex Numbers and Parallel Execution

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Last week, I had the pleasure to dive deep into the Cython world in order to solve a physics problem involving complex numbers. This post describes some of the things I've learnt concerning Cython, complex numbers and parallelization.

What problem am I trying to solve?¶

The problem I was trying to solve was the following: given a vector of complex numbers ($w_i$, "weights"), I wanted to compute its inverse Fourier transform given a certain angular frequency, $\omega$.

So what I want to compute is this:

$$\mathcal{Re}(\sum_{i=1}^n w_i \exp(i \omega t))$$

Beware that $t$ is actually a vector.

Reference implementation with NumPy¶

First, let's see our reference implementation. We make use of NumPy broadcasting to effectively perform the summation part in a single operation over all time steps.

In [1]:
import numpy as np

In [2]:
def synthesis(weights, omega, time_vector):
"""Sums weighted complex exponentials."""
return (weights[:, np.newaxis] * np.exp(1j * omega * time_vector[np.newaxis, :])).sum(axis=0).real


Let's apply this on some sample data.

In [3]:
weights = (np.random.randn(1000) + 1j * np.random.randn(1000)) * np.exp(-np.arange(1000))
omega = 2 * np.pi * 1e-3
time_vector = np.arange(2000, dtype=np.float)

In [4]:
ref_result = synthesis(weights, omega, time_vector)


Let's inspect the shape and the dtype of the result :

In [5]:
ref_result.shape

Out[5]:
(2000,)
In [6]:
ref_result.dtype

Out[6]:
dtype('float64')

This is the expected shape since time step vector is of size 2000. Also, our output is floats, meaning the dtype is as expected. Let's plot the result to have some fun.

In [7]:
import matplotlib.pyplot as plt
plt.style.use('bmh')
%matplotlib inline

In [8]:
plt.plot(time_vector, ref_result)

Out[8]:
[]

Let's move on and port this synthesis function to Cython. We will then time the execution speed and see if Cython allows us to make this function faster.

But first, let's make a detour. To port our function to Cython, we need to be able to do two things:

• use complex number types in Cython
• use the complex exponential "from the C/C++ world"

Prerequesites for Cython & complex numbers¶

Declaring complex numbers in Cython¶

So how do you declare a complex number in Cython? Luckily, the Cython tutorial from Scipy 2017 by Kurt Smith comes to the rescue (specifically, this notebook, but check out the whole tutorial, it's a great learning resource).

In [9]:
%load_ext cython

In [10]:
%%cython -a

# double complex is preferred for compatibility with Python's complex type:
# https://docs.python.org/3/c-api/complex.html

cdef:
float complex fc = 1+1j
double complex dc = 1+1j
long double complex ldc = 1+1j

print(fc, dc, ldc)
print(fc.real, dc.imag, ldc.conjugate())

(1+1j) (1+1j) (1+1j)
1.0 1.0 (1-1j)

Out[10]:
Cython: _cython_magic_535820ea99a6bba3370e40ba9afac1e3.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

 01:
 02: # double complex is preferred for compatibility with Python's complex type:
 03: # https://docs.python.org/3/c-api/complex.html
 04:
 05: cdef:
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 09:
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Okay, but what about complex numbers in a NumPy array? Well, we can use the memoryview syntax for this.

In [11]:
%%cython -a

def identity(complex [::1] weights):
return weights

Out[11]:
Cython: _cython_magic_100be49ff458200c629e7c893dc56a0d.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

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In [12]:
np.allclose(weights, identity(weights))

Out[12]:
True

But we can also use the ndarray syntax:

In [13]:
%%cython -a
cimport numpy as np

def identity(np.ndarray [np.complex128_t, ndim=1] weights):
return weights

Out[13]:
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In [14]:
np.allclose(weights, identity(weights))

Out[14]:
True

We will stick to the above syntax (np.ndarray) in what follows.

The complex exponential¶

Another thing we need is to have access to the complex exponential in the Cython code. A naive approach is to use the NumPy exponetial:

In [15]:
%%cython -a
cimport numpy as np
import numpy as np
import cython

@cython.boundscheck(False)
@cython.wraparound(False)
def apply_complex_exp(np.ndarray [np.complex128_t, ndim=1] weights, np.ndarray [np.complex128_t, ndim=1] out):
cdef int i
for i in range(weights.shape[0]):
out[i] = np.exp(1j * weights[i])

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 03: import cython
 04:
 05: @cython.boundscheck(False)
 06: @cython.wraparound(False)
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In [16]:
out = np.empty_like(weights)

In [17]:
out.dtype

Out[17]:
dtype('complex128')
In [18]:
apply_complex_exp(weights, out)


Let's check the result:

In [19]:
np.allclose(np.exp(1j * weights), out)

Out[19]:
True

However, the problem is that we don't get pure C speed on this operation due to the call to a numpy function. There must be a better way. Searching the internet, I found this StackOverflow thread, which suggests to do the following.

In [20]:
%%cython -a --cplus
cimport numpy as np
import numpy as np
import cython

cdef extern from "complex.h":
double complex exp(double complex)

@cython.boundscheck(False)
@cython.wraparound(False)
def apply_complex_exp2(np.ndarray [np.complex128_t, ndim=1] weights, np.ndarray [np.complex128_t, ndim=1] out):
cdef int i
for i in range(weights.shape[0]):
out[i] = exp(1j * weights[i])

Out[20]:
Cython: _cython_magic_d867f5e67c8f54a1604814be92f853ab.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

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 03: import cython
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 11:     cdef int i
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This allows us translating to pure C... wait-for-it... ++! If you look closely at the above code cell, I had to add the --cplus flag to the compiler since the extern definition we are referring to is the built-in header for the a file that on my computer can be found in Anaconda\Lib\site-packages\Cython\Includes\libcpp! If I don't add this, I get an error from the compiler...

Let's check that this returns the right result.

In [21]:
apply_complex_exp2(weights, out)

In [22]:
np.allclose(np.exp(1j * weights), out)

Out[22]:
True

Let's also check that this is faster:

In [23]:
%timeit apply_complex_exp(weights, out)

1.24 ms ± 100 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [24]:
%timeit apply_complex_exp2(weights, out)

40.8 µs ± 3.87 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)


Indeed, the second approach that uses C++ is much faster!

With these things in mind, let's now write the Cython version of our reference implementation.

Cython optimization of the synthesis function¶

In [25]:
%%cython -a --cplus
cimport numpy as np
import numpy as np
import cython

cdef extern from "" namespace "std" nogil:
double complex exp(double complex z)
double real(double complex z)

cdef double complex I = 1j

@cython.boundscheck(False)
@cython.wraparound(False)
def synthesis_cython1(np.ndarray [np.complex128_t, ndim=1] weights,
double omega,
np.ndarray [np.float64_t, ndim=1] time_vector):
cdef int i, j
cdef np.ndarray [np.float64_t, ndim=1] out = np.empty_like(time_vector, dtype=np.float)
cdef double complex temp_sum
for i in range(time_vector.shape[0]):
temp_sum = 0
for j in range(weights.shape[0]):
temp_sum += weights[j] * exp(I * time_vector[i] * omega )
out[i] = real(temp_sum)
return out

Out[25]:
Cython: _cython_magic_180265c64445be1e3c6d6a7fae5a6faf.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

+01: cimport numpy as np
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 03: import cython
 04:
 05: cdef extern from "" namespace "std" nogil:
 06:     double complex exp(double complex z)
 07:     double real(double complex z)
 08:
+09: cdef double complex I = 1j
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 10:
 11: @cython.boundscheck(False)
 12: @cython.wraparound(False)
+13: def synthesis_cython1(np.ndarray [np.complex128_t, ndim=1] weights,
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 14:                      double omega,
 15:                      np.ndarray [np.float64_t, ndim=1] time_vector):
 16:     cdef int i, j
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In [26]:
out = synthesis_cython1(weights, omega, time_vector)

In [27]:
np.allclose(ref_result, out)

Out[27]:
True
In [28]:
plt.plot(ref_result)
plt.plot(out)

Out[28]:
[]

Let's now do some timings.

In [29]:
%timeit synthesis(weights, omega, time_vector)

30.1 ms ± 625 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [30]:
%timeit synthesis_cython1(weights, omega, time_vector)

127 ms ± 5.81 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)


As we can see, our Cython version is almost ten times slower than our vectorized implementation. Let's try tweaking it.

In [31]:
%%cython -a --cplus
cimport numpy as np
import numpy as np
import cython

cdef extern from "" namespace "std" nogil:
double complex exp(double complex z)
double real(double complex z)

cdef double complex I = 1j

@cython.boundscheck(False)
@cython.wraparound(False)
def synthesis_cython2(np.ndarray [np.complex128_t, ndim=1] weights,
double omega,
np.ndarray [np.float64_t, ndim=1] time_vector):
cdef int i, j
cdef np.ndarray [np.float64_t, ndim=1] out = np.empty_like(time_vector, dtype=np.float)
cdef double complex temp_sum
cdef double complex temp_mult
for i in range(time_vector.shape[0]):
temp_sum = 0
temp_mult = exp(I * time_vector[i] * omega)
for j in range(weights.shape[0]):
temp_sum += weights[j] * temp_mult
out[i] = real(temp_sum)
return out

Out[31]:
Cython: _cython_magic_59850e01233c87321a5c3910c7be7295.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

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 03: import cython
 04:
 05: cdef extern from "" namespace "std" nogil:
 06:     double complex exp(double complex z)
 07:     double real(double complex z)
 08:
+09: cdef double complex I = 1j
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 10:
 11: @cython.boundscheck(False)
 12: @cython.wraparound(False)
+13: def synthesis_cython2(np.ndarray [np.complex128_t, ndim=1] weights,
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Let's check it's correct again:

In [32]:
out = synthesis_cython2(weights, omega, time_vector)

In [33]:
np.allclose(ref_result, out)

Out[33]:
True
In [34]:
%timeit synthesis(weights, omega, time_vector)

29.7 ms ± 573 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [35]:
%timeit synthesis_cython2(weights, omega, time_vector)

27.6 ms ± 392 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)


Nice! Moving the computation of the complex exponent outside of the loop helped us match NumPy. As you can see above however, line 22 is still yellow, meaning there is some Python interaction going on. Can we eliminate that?

In [36]:
%%cython -a --cplus
cimport numpy as np
import numpy as np
import cython

cdef extern from "" namespace "std" nogil:
double complex exp(double complex z)
double real(double complex z)

cdef double complex I = 1j

@cython.boundscheck(False)
@cython.wraparound(False)
def synthesis_cython3(np.ndarray [complex, ndim=1] weights,
double omega,
np.ndarray [double, ndim=1] time_vector):
cdef int i, j
cdef np.ndarray [np.float64_t, ndim=1] out = np.empty_like(time_vector, dtype=np.float)
cdef double complex temp_sum
cdef double complex temp_mult
for i in range(time_vector.shape[0]):
temp_sum = 0
temp_mult = exp(I * time_vector[i] * omega)
for j in range(weights.shape[0]):
temp_sum += weights[j] * temp_mult
out[i] = real(temp_sum)
return out

Out[36]:
Cython: _cython_magic_9686ecbb26e36aaeeb31076454f5407d.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

+01: cimport numpy as np
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 03: import cython
 04:
 05: cdef extern from "" namespace "std" nogil:
 06:     double complex exp(double complex z)
 07:     double real(double complex z)
 08:
+09: cdef double complex I = 1j
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 10:
 11: @cython.boundscheck(False)
 12: @cython.wraparound(False)
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Yes we can! All we did was changing the dtype of the input array. Let's check our function produces correct results again:

In [37]:
out = synthesis_cython3(weights, omega, time_vector)

In [38]:
np.allclose(ref_result, out)

Out[38]:
True
In [39]:
%timeit synthesis(weights, omega, time_vector)

29.9 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [40]:
%timeit synthesis_cython3(weights, omega, time_vector)

28.1 ms ± 1.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)


Interestingly, even though our function is now "completely white", meaning that Cython has translated it to pure C++, it is not actually faster than our previous version. The last thing we can do in our quest for performance is to make the loop parallel.

In [41]:
%%cython -a --cplus --compile-args=/openmp
cimport numpy as np
import numpy as np
import cython
from cython.parallel import prange

cdef extern from "" namespace "std" nogil:
double complex exp(double complex z)
double real(double complex z)

cdef double complex I = 1j

@cython.boundscheck(False)
@cython.wraparound(False)
def synthesis_cython4(np.ndarray [complex, ndim=1] weights,
double omega,
np.ndarray [double, ndim=1] time_vector):
cdef int i, j
cdef np.ndarray [np.float64_t, ndim=1] out = np.empty_like(time_vector, dtype=np.float)
cdef double complex temp_sum
cdef double complex temp_mult
for i in prange(time_vector.shape[0], nogil=True):
temp_sum = 0
temp_mult = exp(I * time_vector[i] * omega)
for j in range(weights.shape[0]):
temp_sum = temp_sum + weights[j] * temp_mult
out[i] = real(temp_sum)
return out

Out[41]:
Cython: _cython_magic_e7cc766498993d3947c8c5bbc875e6a8.pyx

Generated by Cython 0.25.2

Yellow lines hint at Python interaction.
Click on a line that starts with a "+" to see the C code that Cython generated for it.

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 03: import cython
 04: from cython.parallel import prange
 05:
 06: cdef extern from "" namespace "std" nogil:
 07:     double complex exp(double complex z)
 08:     double real(double complex z)
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 13: @cython.wraparound(False)
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 15:                      double omega,
 16:                      np.ndarray [double, ndim=1] time_vector):
 17:     cdef int i, j
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 19:     cdef double complex temp_sum
 20:     cdef double complex temp_mult
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+22:         temp_sum = 0
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+27:     return out
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Note that I had to give up my += term to compile this since I ran into this error message "Cannot read reduction variable in loop body" (fix found here).

Also, note that I supplied the compiler with the --/openmp flag, which is valid under Windows only (for Linux it's --fopenmp).

In [42]:
out = synthesis_cython4(weights, omega, time_vector)

In [43]:
np.allclose(ref_result, out)

Out[43]:
True
In [44]:
%timeit synthesis(weights, omega, time_vector)

29.1 ms ± 250 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [45]:
%timeit synthesis_cython4(weights, omega, time_vector)

9.23 ms ± 521 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)


We achieve a nice little speedup to parallel execution on four cores.

Timings¶

Let's time the different versions of our functions!

In [46]:
timings = {}
for func, label in zip([synthesis, synthesis_cython1, synthesis_cython2, synthesis_cython3, synthesis_cython4],
['reference', 'cython1-naive', 'cython2-clever', 'cython3-clever-pure-c++-loop', 'cython-parallel']):
obj = %timeit -o func(weights, omega, time_vector)
timings[label] = obj

29.3 ms ± 639 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
123 ms ± 3.54 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
27.5 ms ± 352 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
27.3 ms ± 218 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
8.76 ms ± 599 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [47]:
import pandas as pd
s = pd.Series({key: timings[key].average * 1e3 for key in timings}).to_frame(name='timings (ms)').sort_values(by='timings (ms)')
s

Out[47]:
timings (ms)
cython-parallel 8.757900
cython3-clever-pure-c++-loop 27.341302
cython2-clever 27.519439
reference 29.275485
cython1-naive 123.381547
In [48]:
fig, ax = plt.subplots(figsize=(10, 5))
s.plot(kind='bar', ax=ax, rot=45)
plt.legend(loc='upper left')

Out[48]:

Conclusions¶

So here's the list of the things I learnt while doing this work:

• declaring complex variables within Cython functions
• using C++ std lib functions using cdef extern in Cython code
• Cython allows one to easily parallelize code
• the Cython error messages are often cryptic
• it is useful to read the generated Cython code to understand what's going on under the hood of the translation