I recently switched from Matlab to Python. While converting one of my lengthy codes, I was surprised to find Python being very slow. I profiled and traced the problem with one function hogging up time. This function is being called from various places in my code (being part of other functions which are recursively called). Profiler suggests that 300 calls are made to this function in both Matlab and Python.
In short, following codes summarizes the issue at hand:
MATLAB
The class containing the function:
classdef ExampleKernel1 < handle
methods (Static)
function [kernel] = kernel_2D(M,x,N,y)
kernel = zeros(M,N);
for i= 1 : M
for j= 1 : N
% Define the custom kernel function here
kernel(i , j) = sqrt((x(i , 1) - y(j , 1)) .^ 2 + ...
(x(i , 2) - y(j , 2)) .^2 );
end
end
end
end
end
and the script to call test.m:
xVec=[
49.7030 78.9590
42.6730 11.1390
23.2790 89.6720
75.6050 25.5890
81.5820 53.2920
44.9680 2.7770
38.7890 78.9050
39.1570 33.6790
33.2640 54.7200
4.8060 44.3660
49.7030 78.9590
42.6730 11.1390
23.2790 89.6720
75.6050 25.5890
81.5820 53.2920
44.9680 2.7770
38.7890 78.9050
39.1570 33.6790
33.2640 54.7200
4.8060 44.3660
];
N=size(xVec,1);
kex1=ExampleKernel1;
tic
for i=1:300
K=kex1.kernel_2D(N,xVec,N,xVec);
end
toc
Gives the output
clear all
>> test
Elapsed time is 0.022426 seconds.
>> test
Elapsed time is 0.009852 seconds.
PYTHON 3.4
Class containing the function CustomKernels.py:
from numpy import zeros
from math import sqrt
class CustomKernels:
"""Class for defining the custom kernel functions"""
@staticmethod
def exampleKernelA(M, x, N, y):
"""Example kernel function A"""
kernel = zeros([M, N])
for i in range(0, M):
for j in range(0, N):
# Define the custom kernel function here
kernel[i, j] = sqrt((x[i, 0] - y[j, 0]) ** 2 + (x[i, 1] - y[j, 1]) ** 2)
return kernel
and the script to call test.py:
import numpy as np
from CustomKernels import CustomKernels
from time import perf_counter
xVec = np.array([
[49.7030, 78.9590],
[42.6730, 11.1390],
[23.2790, 89.6720],
[75.6050, 25.5890],
[81.5820, 53.2920],
[44.9680, 2.7770],
[38.7890, 78.9050],
[39.1570, 33.6790],
[33.2640, 54.7200],
[4.8060 , 44.3660],
[49.7030, 78.9590],
[42.6730, 11.1390],
[23.2790, 89.6720],
[75.6050, 25.5890],
[81.5820, 53.2920],
[44.9680, 2.7770],
[38.7890, 78.9050],
[39.1570, 33.6790],
[33.2640, 54.7200],
[4.8060 , 44.3660]
])
N = xVec.shape[0]
kex1 = CustomKernels.exampleKernelA
start=perf_counter()
for i in range(0,300):
K = kex1(N, xVec, N, xVec)
print(' %f secs' %(perf_counter()-start))
Gives the output
%run test.py
0.940515 secs
%run test.py
0.884418 secs
%run test.py
0.940239 secs
RESULTS
Comparing the results it seems Matlab is about 42 times faster after a "clear all" is called and then 100 times faster if script is run multiple times without calling "clear all". That is at least and order of magnitude if not two orders of magnitudes faster. This is a very surprising result for me. I was expecting the result to be the other way around.
Can someone please shed some light on this?
Can someone suggest a faster way to perform this?
SIDE NOTE
I have also tried to use numpy.sqrt which makes the performance worse, therefore I am using math.sqrt in Python.
EDIT
The for loops for calling the functions are purely fictitious. They are there just to "simulate" 300 calls to the function. As I described earlier, the kernel functions (kernel_2D in Matlab and kex1 in Python) are called from various different places in the program. To make the problem shorter, I "simulate" the 300 calls using the for loop. The for loops inside the kernel functions are essential and unavoidable because of the structure of the kernel matrix.
EDIT 2
Here is the larger problem: https://github.com/drfahdsiddiqui/bbfmm2d-python
You want to get rid of those for loops. Try this:
def exampleKernelA(M, x, N, y):
"""Example kernel function A"""
i, j = np.indices((N, M))
# Define the custom kernel function here
kernel[i, j] = np.sqrt((x[i, 0] - y[j, 0]) ** 2 + (x[i, 1] - y[j, 1]) ** 2)
return kernel
You can also do it with broadcasting, which may be even faster, but a little less intuitive coming from MATLAB.
Upon further investigation I have found that using indices as indicated in the answer is still slower.
Solution: Use meshgrid
def exampleKernelA(M, x, N, y):
"""Example kernel function A"""
# Euclidean norm function implemented using meshgrid idea.
# Fastest
x0, y0 = meshgrid(y[:, 0], x[:, 0])
x1, y1 = meshgrid(y[:, 1], x[:, 1])
# Define custom kernel here
kernel = sqrt((x0 - y0) ** 2 + (x1 - y1) ** 2)
return kernel
Result: Very very fast, 10 times faster than indices approach. I am getting times which are closer to C.
However: Using meshgrid with Matlab beats C and Numpy by being 10 times faster than both.
Still wondering why!
Matlab uses commerical MKL library. If you use free python distribution, check whether you have MKL or other high performance blas library used in python or it is the default ones, which could be much slower.
Comparing Jit-Compilers
It has been mentioned that Matlab uses an internal Jit-compiler to get good performance on such tasks. Let's compare Matlabs jit-compiler with a Python jit-compiler (Numba).
Code
import numba as nb
import numpy as np
import math
import time
#If the arrays are somewhat larger it makes also sense to parallelize this problem
#cache ==True may also make sense
@nb.njit(fastmath=True)
def exampleKernelA(M, x, N, y):
"""Example kernel function A"""
#explicitly declaring the size of the second dim also improves performance a bit
assert x.shape[1]==2
assert y.shape[1]==2
#Works with all dtypes, zeroing isn't necessary
kernel = np.empty((M,N),dtype=x.dtype)
for i in range(M):
for j in range(N):
# Define the custom kernel function here
kernel[i, j] = np.sqrt((x[i, 0] - y[j, 0]) ** 2 + (x[i, 1] - y[j, 1]) ** 2)
return kernel
def exampleKernelB(M, x, N, y):
"""Example kernel function A"""
# Euclidean norm function implemented using meshgrid idea.
# Fastest
x0, y0 = np.meshgrid(y[:, 0], x[:, 0])
x1, y1 = np.meshgrid(y[:, 1], x[:, 1])
# Define custom kernel here
kernel = np.sqrt((x0 - y0) ** 2 + (x1 - y1) ** 2)
return kernel
@nb.njit()
def exampleKernelC(M, x, N, y):
"""Example kernel function A"""
#explicitly declaring the size of the second dim also improves performance a bit
assert x.shape[1]==2
assert y.shape[1]==2
#Works with all dtypes, zeroing isn't necessary
kernel = np.empty((M,N),dtype=x.dtype)
for i in range(M):
for j in range(N):
# Define the custom kernel function here
kernel[i, j] = np.sqrt((x[i, 0] - y[j, 0]) ** 2 + (x[i, 1] - y[j, 1]) ** 2)
return kernel
#Your test data
xVec = np.array([
[49.7030, 78.9590],
[42.6730, 11.1390],
[23.2790, 89.6720],
[75.6050, 25.5890],
[81.5820, 53.2920],
[44.9680, 2.7770],
[38.7890, 78.9050],
[39.1570, 33.6790],
[33.2640, 54.7200],
[4.8060 , 44.3660],
[49.7030, 78.9590],
[42.6730, 11.1390],
[23.2790, 89.6720],
[75.6050, 25.5890],
[81.5820, 53.2920],
[44.9680, 2.7770],
[38.7890, 78.9050],
[39.1570, 33.6790],
[33.2640, 54.7200],
[4.8060 , 44.3660]
])
#compilation on first callable
#can be avoided with cache=True
res=exampleKernelA(xVec.shape[0], xVec, xVec.shape[0], xVec)
res=exampleKernelC(xVec.shape[0], xVec, xVec.shape[0], xVec)
t1=time.time()
for i in range(10_000):
res=exampleKernelA(xVec.shape[0], xVec, xVec.shape[0], xVec)
print(time.time()-t1)
t1=time.time()
for i in range(10_000):
res=exampleKernelC(xVec.shape[0], xVec, xVec.shape[0], xVec)
print(time.time()-t1)
t1=time.time()
for i in range(10_000):
res=exampleKernelB(xVec.shape[0], xVec, xVec.shape[0], xVec)
print(time.time()-t1)
Performance
exampleKernelA: 0.03s
exampleKernelC: 0.03s
exampleKernelB: 1.02s
Matlab_2016b (your code, but 10000 rep., after few runs): 0.165s
I got ~5x speed improvement over the meshgrid solution using only broadcasting:
def exampleKernelD(M, x, N, y):
return np.sqrt((x[:,1:] - y[:,1:].T) ** 2 + (x[:,:1] - y[:,:1].T) ** 2)
来源：https://stackoverflow.com/questions/46475162/performance-matlab-vs-python