Computing time for .^(1/2) or .^(1/p)
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Maxime Polichetti
on 29 Apr 2019
Commented: Walter Roberson
on 8 May 2019
Hi,
I realised that computing the square root element-wise of a matrix using .^(1/2) is faster than computing the pth root whatever the value of p :
I found (with the provided code below):
Averaged time for A.^(1/2): 0.0337 s
Averaged time for A.^(1/3): 0.0972 s
Averaged time for A.^(1/5): 0.0946 s
Does anyone know why ? (I am clearly not an expert in algorithm complexity and optimization, but I'd like to know if there is a special case of the square root implemented in the function (.^), and eventually to understand how it works in a simple way).
Thanks in advance :) !
clear all
close all
clc
% Data
A = randn(1024,1024);
% Averaged time to Compute the square root of each element in A
t_squareroot = 0;
for u = 1 : 50
tic
B = A.^(1/2);
t_squareroot = t_squareroot + toc/50;
end
disp('Averaged time for A.^(1/2):')
disp(t_squareroot)
% Averaged time to Compute the cubic root of each element in A
t_cubicroot = 0;
for u = 1 : 50
tic
C = A.^(1/3);
t_cubicroot = t_cubicroot + toc/50;
end
disp('Averaged time for A.^(1/3)')
disp(t_cubicroot)
% Averaged time to Compute the 5th root of each element in A
t_5throot = 0;
for u = 1 : 50
tic
D = A.^(1/5);
t_5throot = t_5throot + toc/50;
end
disp('Averaged time for A.^(1/5)')
disp(t_5throot)
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Accepted Answer
Stephane Dauvillier
on 29 Apr 2019
In order to compute the square root of any number you only need to be able to compute the square root of the mantisse (which is between 1 and 2). You only have to subtract 1 to the exposant which is absolutly costless.
There is no such thing for the other power.
7 Comments
Walter Roberson
on 8 May 2019
In situations such as these, it is not necessarily the case that similar speed-ups could not be found for other specific roots, but rather that they may need custom coding for each different root, and so there become questions of whether it is worth writing any particular one -- is the programming/debugging time and code maintenance time worth the extra performance compared to the amount of time that particular exponent is used?
sqrt() gets used a lot, so it is a natural. Cube root... maybe . 4th root... I do see that used, but I probably see random non-integer roots used more often.
More Answers (2)
James Tursa
on 29 Apr 2019
Edited: James Tursa
on 29 Apr 2019
I am unaware of any documentation on how TMW has chosen to implement their rooting/power algorithms, but it would not surprise me that they use a custom fast algorithm for sqrt vs a slower more generic algorithm for other powers & roots. E.g., one could use a rational function approximation for the mantissa combined with direct exponent bit manipulation for square root, and then something else for all the other powers. (This is just an example of what could be done ... not necessarily my guess at what the library code that MATLAB uses does do).
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Walter Roberson
on 29 Apr 2019
Edited: Walter Roberson
on 29 Apr 2019
https://www.felixcloutier.com/x86/sqrtpd (hardware square root)
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