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Matlab vs python syntax11/25/2023 ![]() #If the arrays are somewhat larger it makes also sense to parallelize this problem Let's compare Matlabs jit-compiler with a Python jit-compiler (Numba). It has been mentioned that Matlab uses an internal Jit-compiler to get good performance on such tasks. The for loops inside the kernel functions are essential and unavoidable because of the structure of the kernel matrix. To make the problem shorter, I " simulate" the 300 calls using the for loop. ![]() As I described earlier, the kernel functions ( kernel_2D in Matlab and kex1 in Python) are called from various different places in the program. They are there just to " simulate" 300 calls to the function. The for loops for calling the functions are purely fictitious. I have also tried to use numpy.sqrt which makes the performance worse, therefore I am using math.sqrt in Python. I was expecting the result to be the other way around.Ĭan someone please shed some light on this?Ĭan someone suggest a faster way to perform this? That is at least and order of magnitude if not two orders of magnitudes faster. Print(' %f secs' %(perf_counter()-start))Ĭomparing 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". Kernel = sqrt((x - y) ** 2 + (x - y) ** 2)Īnd the script to call test.py: import numpy as np """Class for defining the custom kernel exampleKernelA(M, x, N, y): The class containing the function: classdef ExampleKernel1 > testĬlass containing the function CustomKernels.py: from numpy import zeros In short, following codes summarizes the issue at hand: Profiler suggests that 300 calls are made to this function in both Matlab and Python. This function is being called from various places in my code (being part of other functions which are recursively called). I profiled and traced the problem with one function hogging up time. While converting one of my lengthy codes, I was surprised to find Python being very slow. I recently switched from Matlab to Python.
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