Here is a nested loop, where the inner one indices depend on the outer one, with the following parameters:
f = rand(1,70299)
nech=24*30*24
N=length(f);
xh=(1:nech)/24;
In MATLAB:
sf2(1:nech)=0.;
sf2vel(1:nech)=0.;
count(1:nech)=0.;
for i=1:nech
for j=1:N-i-1
sf2(i)=sf2(i) (f(j i)-f(j))^2;
count(i)=count(i) 1;
end
sf2(i)=sf2(i)/count(i);
end
In Python:
def structFunPython(f,N,nech):
sf2 = np.zeros(N)
count = np.zeros(N)
for i in range(nech):
indN = np.arange(1,N-i-1)
for j in indN:
sf2[i] = np.power((f[i j]-f[j]),2)
count[i] = 1
sf2[i] = sf2[i]/count[i]
return sf2
With cython:
import cython
cimport numpy as np
import numpy as np
def structFun(np.ndarray f,N,nech):
cdef np.ndarray sf2 = np.zeros(N), count = np.zeros(N),
for i in range(nech):
indN = np.arange(1,N-i-1)
for j in indN:
sf2[i] = np.power((f[i j]-f[j]),2)
count[i] = 1
sf2[i] = sf2[i]/count[i]
return sf2
Executions times:
Matlab: 7.8377 sec
Python: 3651.35 sec
Cython: 3336.21 sec
I have a hard time believing Python and Cython (especially Cython) are that slow for the same computation, so I think I must have made an error in my Python/Cython loops, but I can't see where.
CodePudding user response：
Disclaimer: as @norok2 pointed out in a comment, my approach allocates at least N*(N-1)/2 doubles due to the use of pdist. For your N = 70299 this means about 18.5 GB of doubles in an array. Other index arrays will have similar size. So unless some of your use cases have smaller N, the vectorised approach in this answer is only feasible if you have a lot of memory.
As others have noted, just translating code from one language to another will not lead to optimal code in both languages. And using Cython alone is no guarantee for speed-up, in the same way that using NumPy alone is no guarantee for speed-up.
norok2's answer nicely shows you how you can use numba or something similar to compile your numerical code. This can give you something quite similar to what you have in MATLAB in terms of performance, since MATLAB has a just-in-time (JIT) compiler of its own. There's also wiggle room to optimize your code for compilation, because multiple implementations can end up with wildly different performance.
Anyway, I want to make the point that you can also speed up your code by using advanced features in NumPy and SciPy. In particular, what you want is to compute pairwise squared distances among a set of 1d points. This is what scipy.spatial.distance.pdist can do for you (with the 'sqeuclidean' squared Euclidean norm). The upside is that it only compute each pairwise distance once (which is great for CPU and memory performance), but the downside is that picking out the contributions that you want to sum up a bit more cumbersome.
Anyway, here is the code corresponding to your Python implementation (with the fix that the inner loop uses np.arange(1, N-i) rather than np.arange(1, N-i-1)):
from scipy.spatial.distance import pdist
def pdisty(f, nech):
offset = f.size - nech
res = np.zeros_like(f, shape=nech)
dists = pdist(f[:, None], metric='sqeuclidean')
counts = np.arange(offset, f.size)[::-1]
inds = np.repeat(np.arange(res.size), counts)
np.add.at(res, inds, dists[:inds.size])
res /= counts
return res
What happens here is that
- we compute the pairwise distance of each unique pair of array values and store it to
dists - we compute the number of pairs each point is involved in (this is what we'll have to normalise at the end), store it to
counts - figure out the 1d index of each value in
diststhat it corresponds to (this is the hard part), store it ininds - use
np.add.atto accumulate each contribution to the appropriate output index - normalise with the counts.
Here are some timings for N = 1000, where func2() is the corresponding function from norok2's answer:
>>> %timeit structFunPython(f, f.size - 1)
... %timeit func2(f, f.size - 1)
... %timeit pdisty(f, f.size - 1)
1.48 s ± 89.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
274 ms ± 2.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
36.7 ms ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
The above solution is considerably faster, but of course it's still slower than a fully compiled solution. If you have an issue with additional dependencies or getting llvm installed on your system, this might be a reasonable compromise. The bottom line is that code should be adapted to the language you're trying to optimise it in.
For completeness' sake here are the implementations I used for the comparison (I slightly changed the signatures because N can be computed from the input array, and I fixed some fencepost errors):
def structFunPython(f, nech):
"""Very slightly modified from the question"""
N = f.size
sf2 = np.zeros(nech)
count = np.zeros(nech)
for i in range(nech):
indN = np.arange(1,N-i)
for j in indN:
sf2[i] = np.power((f[i j]-f[i]),2)
count[i] = 1
sf2[i] = sf2[i]/count[i]
return sf2
def func2(f_arr, nech):
"""Very slightly modified from norok2's answer
See https://stackoverflow.com/a/71704834/5067311
"""
n = f_arr.size
sf2 = np.zeros(nech)
for i in range(nech):
for j in range(1, n - i):
sf2[i] = (f_arr[i j] - f_arr[i]) ** 2
sf2[i] /= (n - i - 1)
return sf2
With these definitions all three functions give the same result within machine precision:
rng = np.random.default_rng()
N = 1000
nech = N - 2
f = rng.random(1000)
assert np.allclose(structFunPython(f, nech), func2(f, nech))
assert np.allclose(structFunPython(f, nech), pdisty(f, nech))
CodePudding user response：
The way I would rewrite the MATLAB code as equivalent Python code could be (beware of indices starting at 1 in MATLAB and at 0 in Python... since I cannot know how this should be adapted without context I went with the simplest approach):
import numpy as np
def func(f_arr, nech, n):
sf2 = np.zeros(nech)
count = np.zeros(nech)
for i in range(nech):
for j in range(n - i):
sf2[i] = (f_arr[i j] - f_arr[i]) ** 2
count[i] = 1
sf2[i] /= count[i]
return sf2
Note that count[i] = 1 is useless as the final value of count[i] is known in advance, and actually the whole count is useless, e.g.:
import numpy as np
def func2(f_arr, nech, n):
sf2 = np.zeros(nech)
for i in range(nech):
for j in range(n - i):
sf2[i] = (f_arr[i j] - f_arr[i]) ** 2
sf2[i] /= (n - i)
return sf2
Speed-up
This a manual case of Numba speed up. This is just as easy as adding/using a Numba @njit decorator:
import numba as nb
func_nb = nb.njit(func)
func2_nb = nb.njit(func2)
Now, func, func_nb, func2 and func2_nb all perform the same computation:
nech = n = 6
f_arr = np.arange(n)
print(func(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
print(func_nb(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
print(func2(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
print(func2_nb(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
If you really need to stick to Cython, here is an implementation based on func2:
%%cython -c-O3 -c-march=native -a
#cython: language_level=3, boundscheck=False, wraparound=False, initializedcheck=False, cdivision=True, infer_types=True
import numpy as np
import cython as cy
cpdef func2_cy(f_arr, nech, n):
sf2 = np.zeros(nech)
_func2_cy(f_arr.astype(np.float_), sf2, nech, n)
return sf2
cdef _func2_cy(double[:] f_arr, double[:] sf2, cy.int nech, cy.int n):
for i in range(nech):
for j in range(1, n - i):
sf2[i] = sf2[i] (f_arr[i j] - f_arr[i]) ** 2
sf2[i] = sf2[i] / (n - i)
This is significantly more complicated to write compared to Numba, but achieves similar performances.
The trick is to have a function _func2_cy which operates with little to no interaction with Python (read: it goes at C speed).
Again the result is the same as func2:
nech = n = 6
f_arr = np.arange(n)
print(func2_cy(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
Timings
With some little toy benchmarking we get a feeling of the speed-ups, including a comparable function written as you did, and the vectorized solution proposed in Andras Deak's very fine answer (but fixing the indices to match the above):
def func_OP(f, nech, n):
sf2 = np.zeros(n)
count = np.zeros(n)
for i in range(nech):
indN = np.arange(n - i) # <-- indexing fixed
for j in indN:
sf2[i] = np.power((f[i j]-f[i]),2)
count[i] = 1
sf2[i] = sf2[i] / count[i]
return sf2
func_OP_nb = nb.njit(func_OP)
def func_pdisty(f, nech, n):
res = np.zeros(nech)
dists = scipy.spatial.distance.pdist(f[:, None], metric='sqeuclidean')
counts = np.arange(n - 1, n - nech - 1, -1)
inds = np.repeat(np.arange(res.size), counts)
np.add.at(res, inds, dists[:inds.size])
res /= (counts 1)
return res
nech = n = 6
f_arr = np.arange(n)
print(func_OP(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
print(func_pdisty(f_arr, nech, n))
# [9.16666667 6. 3.5 1.66666667 0.5 0. ]
nech = n = 1000
f_arr = np.arange(n)
%timeit func_OP(f_arr, nech, n)
# 1 loop, best of 5: 1.5 s per loop
%timeit func(f_arr, nech, n)
# 1 loop, best of 5: 567 ms per loop
%timeit func2(f_arr, nech, n)
# 1 loop, best of 5: 352 ms per loop
%timeit func_OP_nb(f_arr, nech, n)
# 1000 loops, best of 5: 1.87 ms per loop
%timeit func_nb(f_arr, nech, n)
# 1000 loops, best of 5: 1.7 ms per loop
%timeit func2_nb(f_arr, nech, n)
# 1000 loops, best of 5: 768 µs per loop
%timeit func_pdisty(f_arr, nech, n)
# 10 loops, best of 5: 44.5 ms per loop
%timeit func2_cy(f_arr, nech, n)
# 1000 loops, best of 5: 1 ms per loop
nech = n = 2000
f_arr = np.arange(n)
%timeit func_OP(f_arr, nech, n)
# 1 loop, best of 5: 6.01 s per loop
%timeit func(f_arr, nech, n)
# 1 loop, best of 5: 2.3 s per loop
%timeit func2(f_arr, nech, n)
# 1 loop, best of 5: 1.42 s per loop
%timeit func_OP_nb(f_arr, nech, n)
# 100 loops, best of 5: 7.31 ms per loop
%timeit func_nb(f_arr, nech, n)
# 100 loops, best of 5: 6.82 ms per loop
%timeit func2_nb(f_arr, nech, n)
# 100 loops, best of 5: 3.05 ms per loop
%timeit func_pdisty(f_arr, nech, n)
# 1 loop, best of 5: 344 ms per loop
%timeit func2_cy(f_arr, nech, n)
# 100 loops, best of 5: 3.95 ms per loop
nech = n = 4000
f_arr = np.arange(n)
%timeit func_OP(f_arr, nech, n)
# 1 loop, best of 5: 24.3 s per loop
%timeit func(f_arr, nech, n)
# 1 loop, best of 5: 9.27 s per loop
%timeit func2(f_arr, nech, n)
# 1 loop, best of 5: 5.71 s per loop
%timeit func_OP_nb(f_arr, nech, n)
# 10 loops, best of 5: 29 ms per loop
%timeit func_nb(f_arr, nech, n)
# 10 loops, best of 5: 27.3 ms per loop
%timeit func2_nb(f_arr, nech, n)
# 100 loops, best of 5: 12.2 ms per loop
%timeit func_pdisty(f_arr, nech, n)
# 1 loop, best of 5: 706 ms per loop
%timeit func2_cy(f_arr, nech, n)
# 100 loops, best of 5: 15.9 ms per loop
...and with the input sizes you provided:
nech = 24 * 30 * 24
n = 70299
f_arr = np.random.random(n)
%timeit -n1 -r1 func_OP(f_arr, nech, n)
# 1 loop, best of 1: 1h 4min 50s per loop
%timeit -n1 -r1 func(f_arr, nech, n)
# 1 loop, best of 1: 21min 14s per loop
%timeit -n1 -r1 func2(f_arr, nech, n) # only one run / loop
# 1 loop, best of 1: 13min 9s per loop
%timeit func_OP_nb(f_arr, nech, n)
# 1 loop, best of 5: 4.74 s per loop
%timeit func_nb(f_arr, nech, n)
# 1 loop, best of 5: 4 s per loop
%timeit func2_nb(f_arr, nech, n)
# 1 loop, best of 5: 1.62 s per loop
# %timeit func_pdisty(f_arr, nech, n)
# -- MEMORY ERROR --
%timeit func2_cy(f_arr, nech, n)
# 1 loop, best of 5: 2.2 s per loop
CodePudding user response：
You already got some nice answers that presented fast numpy and numba implementations. I'd like to present a decent Cython implementation for a fair comparison.
First of all, let's time norok2's fastest numba implementation on my machine:
In [3]: %timeit func2_nb(f_arr, n)
3.4 s ± 129 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In order to obtain fast memory access in Cython, it's crucial to give it all the necessary information about the input arrays. In your case,
f_arris a C-contiguous np.ndarray, so we use a C-contiguous memoryviewsdouble[::1]instead of normal memoryviewsdouble[:]. The difference is that indexing a C-contiguous memoryview reduces to plain C codef_arr[i]while the latter reduces tof_arr[i f_arr.strides[0]].Next, it's worth mentioning that the Python power operator
a**2is gonna be substituted by the C codepow(a,2), i.e. we call thepow-function. Calling a function in tight loops has unnecessary overhead, even in C. In C, we would just writea*a. So let's do the same in Cython.
In Code:
%%cython -c=-O3 -c=-march=native
# for MSVC use: -c=/Ox -c=/arch:AVX2
# or: -c=/Ox -c=/arch:AVX512 if your CPU supports AVX512
import numpy as np
cimport numpy as np
cimport cython
@cython.boundscheck(False)
@cython.wraparound(False)
def func2_cy(double[::1] f_arr, int nech):
cdef int n = f_arr.size
cdef double[::1] sf2 = np.zeros(nech)
cdef int i, j
for i in range(nech):
for j in range(n-i-2):
sf2[i] = (f_arr[i j 1]-f_arr[i])*(f_arr[i j 1]-f_arr[i])
sf2[i] /= (n - i - 2)
return np.asarray(sf2)
Compiled on my machine with Apple Clang 13.1.6 on macOS, this is slower than the aforementioned numba implementation:
In [5]: %timeit func2_cy(f_arr, n)
4.2 s ± 32 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
However, one should be aware that Cython is basically just a Python to C compiler. This means that after we removed all the python interactions, we can try the same performance tricks we'd apply when using C code: passing the optimization flag -O3 and enabling all CPU instructions available on the current platform -march=native. Note that this also implies that the performance of good Cython code (i.e. code with no python interactions in tight loops) heavily depends on your C Compiler. In my experience, numba is often faster than Cython on Windows due to MSVC's bad auto-vectorization. On macOS/Linux and gcc or clang, it's often the other way around.
One of these well-known performance tricks is loop-unrolling in order to give the compiler hints to SIMD-vectorize the specific loop. Unrolling the innermost loop, the function looks like this:
%%cython -c=-O3 -c=-march=native
# for MSVC use: -c=/Ox -c=/arch:AVX2
# or: -c=/Ox -c=/arch:AVX512 if your CPU supports AVX512
#cython: cdivision=True
import numpy as np
cimport numpy as np
cimport cython
@cython.boundscheck(False)
@cython.wraparound(False)
def func3_cy(double[::1] f_arr, int nech):
cdef int n = f_arr.size
cdef double[::1] sf2 = np.zeros(nech)
cdef double fi, fij, fij1, fij2, fij3
cdef int i, j, ntmp
for i in range(nech):
# unroll the inner loop for better SIMD vectorization
j = 1
ntmp = (n-i-2) - ((n-i-2) % 4)
# we use a while-loop since Cython doesn't support range(j, ntmp, 4)
while j < ntmp:
# unpack the values and hope the CPU keeps them in its register
fi = f_arr[i]
fij = f_arr[i j 1]
fij1 = f_arr[i j 2]
fij2 = f_arr[i j 3]
fij3 = f_arr[i j 4]
sf2[i] = ((fij-fi)*(fij-fi) (fij1-fi)*(fij1-fi) (fij2-fi)*(fij2-fi) (fij3-fi)*(fij3-fi))
j = 4
for j in range(ntmp, n - i - 2):
sf2[i] = (f_arr[i j 1] - f_arr[i]) * (f_arr[i j 1] - f_arr[i])
sf2[i] /= (n-i-2)
return np.asarray(sf2)
On my machine, this is nearly two times faster than numba:
In [7]: %timeit func3_cy(f_arr, n)
1.7 s ± 54.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
To take it one step further, we can parallelize the outer loop with the help of prange which is just a thin wrapper around OpenMP:
%%cython -c=-O3 -c=-march=native -c=-fopenmp --link-args=-fopenmp
# for MSVC use: -c=/Ox -c=/openmp -c=/arch:AVX2
# or: -c=/Ox -c=/openmp -c=/arch:AVX512 if your CPU supports AVX512
#cython: cdivision=True
import numpy as np
cimport numpy as np
cimport cython
from cython.parallel cimport prange
@cython.boundscheck(False)
@cython.wraparound(False)
def func4_cy(double[::1] f_arr, int nech):
cdef int n = f_arr.size
cdef double[::1] sf2 = np.zeros(nech)
cdef double fi, fij, fij1, fij2, fij3
cdef int i, j, ntmp
for i in prange(nech, nogil=True, schedule="static", chunksize=1):
# unroll the inner loop for better SIMD vectorization
j = 1
ntmp = (n-i-2) - ((n-i-2) % 4)
# we use a while-loop since Cython doesn't support range(j, ntmp, 4)
while j < ntmp:
# unpack the values and hope the CPU keeps them in its register
fi = f_arr[i]
fij = f_arr[i j 1]
fij1 = f_arr[i j 2]
fij2 = f_arr[i j 3]
fij3 = f_arr[i j 4]
sf2[i] = ((fij-fi)*(fij-fi) (fij1-fi)*(fij1-fi) (fij2-fi)*(fij2-fi) (fij3-fi)*(fij3-fi))
j = 4
for j in range(ntmp, n - i - 2):
sf2[i] = (f_arr[i j 1] - f_arr[i]) * (f_arr[i j 1] - f_arr[i])
sf2[i] /= (n-i-2)
return np.asarray(sf2)
On my machine with 8 CPU Cores, this is by far the fastest implementation:
In [9]: %timeit func4_cy(f_arr, n)
329 ms ± 15.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
However, it's worth mentioning that Numba also supports thread parallelism, so I'd expect a similar performance with Numba.
CodePudding user response：
With some basic python/numpy rewrites I can speed up your code quite a bit
def structFunPython4(f,N,nech):
sf2 = np.zeros(N)
#count = np.zeros(N)
for i in range(nech):
# indN = np.arange(1,N-i-1)
sf2[i] = np.sum(np.power(f[i 1:N-1]-f[i],2))
#for j in range(1,N-i-1):
# sf2[i] = np.power((f[i j]-f[i]),2)
# #count[i] = 1
sf2[i] = sf2[i]/(N-i-2)
return sf2
For a modest sample size:
In [53]: f = np.arange(100); N=f.shape[0]; nech=98
they match:
In [54]: np.allclose(structFunPython(f,N,nech),structFunPython4(f,N,nech))
Out[54]: True
and timings:
In [55]: timeit structFunPython(f,N,nech)
34.4 ms ± 109 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [56]: timeit structFunPython4(f,N,nech)
2.22 ms ± 35.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
I first replaced for i in indN: with for i in range(1,N-1-i):. Iteration on an array is slower than iteration on a list or range.
As others noted we don't need to iterate the count.
But the big change is to replace the j iteration with an array slice, power on the whole slice and array sum.
I haven't looked at the i iteration enough to eliminate that. f[i 1:N-1] slice varies in length, from nech down to 0.
MATLAB does a lot of jit compilation, so you can get by with iterations. Back in the 1990s when I worked MATLAB that would have been bad form - those versions required whole-matrix calculations to get any reasonable speed. Python level iteration is slow (interpreted), and slower on arrays than on lists. Try to use whole-array methods where possible. Or use numba to compile the calculations.
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