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How to store and reuse coefficients in a for loop

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Matthew Kehoe
Matthew Kehoe on 23 Jul 2021
Edited: per isakson on 25 Jul 2021
My Matlab code has a subroutine that repeatedly executes a double for loop. While testing large simulations, this subroutine is called over 10^6 times. I am curious if I can change/reuse part of the subroutine so that I can improve performance.
My original Matlab code (with test data) is shown below.
% Test Data
clc; clear all;
M = 30;
N = 30;
Nx = 32;
f_n_m = rand(Nx,N+1,M+1);
epsilon = 0.3;
delta = 0.4;
SumType = randi(3,1);
f = zeros(Nx,1);
coeff = zeros(N+1,M+1);
% Original Implementation
for j=1:Nx
% Can this double for loop be performed once instead of Nx times?
for r=0:N
for s=0:M
coeff(r+1,s+1) = f_n_m(j,r+1,s+1);
end
end
if(SumType==1)
% My code calls seperate subroutines which need the value of coeff.
% For testing purposes I have put f(j) = test data. These three if
% statements call external functions in my code.
f(j) = coeff(j)*epsilon*delta*N*M;
elseif(SumType==2)
f(j) = 2*coeff(j)*epsilon*delta*N*M;
elseif(SumType==3)
f(j) = 3*coeff(j)*epsilon*delta*N*M;
end
end
My first (bad) idea was to change the above code to the code below.
f2 = zeros(Nx,1);
coeff2 = zeros(N+1,M+1);
% Move the double for loop here
for j=1:Nx
for r=0:N
for s=0:M
coeff2(r+1,s+1) = f_n_m(j,r+1,s+1);
end
end
end
% Then run the original for loop
for j=1:Nx
if(SumType==1)
f2(j) = coeff2(j)*epsilon*delta*N*M;
elseif(SumType==2)
f2(j) = 2*coeff2(j)*epsilon*delta*N*M;
elseif(SumType==3)
f2(j) = 3*coeff2(j)*epsilon*delta*N*M;
end
end
% However, this gives different answers since f(j) runs against a single
% value of j while f2(j) computes all the values of j.
diff = norm(f-f2,inf) % large
I'm curious if there is a more efficient way of writing
for j=1:Nx
% start inner double for loop
for r=0:N
for s=0:M
coeff(r+1,s+1) = f_n_m(j,r+1,s+1);
end
end
% end inner double for loop
if(SumType==1)
f(j) = % An external function involving coeff, epsilon, delta, N, M
elseif(SumType==2)
f2(j) = % A different external function involving coeff, epsilon, delta, N, M
elseif(SumType==3)
f2(j) = % A third external function involving coeff, epsilon, delta, N, M
end
end
so that the inner double for loop is only calculated once instead of Nx times. Is this a limitation of the way the function is written?

Accepted Answer

per isakson
per isakson on 23 Jul 2021
Caveat: I don't fully understand your code and what I say might not be relevant to your real project.
"% Can this double for loop be performed once instead of Nx times?" The short answer is no, because coeff is 2D and f_n_m is 3D. Maybe, you can make coeff 3D. Depends on how you will use coeff.
"f(jj) = coeff(jj)*epsilon*delta*N*M;" What is coeff(jj) supposed to return? This is linear indexing, which returns a scalar.
Proposal: Replace the double for-loop by alt_coeff = f_n_m( :, :, jj );. Notice that I have modified the indexing of f_n_m so that jj is the third index. Run the function cssm1() with profile(). alt_coeff improves speed significantly.
cssm1
ans = "Happy end"
function out = cssm1
% Test Data
% clc; clear all;
M = 30;
N = 30;
Nx = 32;
f_n_m = rand(N+1,M+1,Nx);
epsilon = 0.3;
delta = 0.4;
SumType = randi(3,1);
f = zeros(Nx,1);
coeff = zeros(N+1,M+1);
% Original Implementation
for jj=1:Nx
% Can this double for loop be performed once instead of Nx times?
for r=0:N
for s=0:M
coeff(r+1,s+1) = f_n_m(r+1,s+1,jj);
end
end
alt_coeff = f_n_m( :, :, jj );
if(SumType==1)
% My code calls seperate subroutines which need the value of coeff.
% For testing purposes I have put f(j) = test data. These three if
% statements call external functions in my code.
f(jj) = coeff(jj)*epsilon*delta*N*M;
elseif(SumType==2)
f(jj) = 2*coeff(jj)*epsilon*delta*N*M;
elseif(SumType==3)
f(jj) = 3*coeff(jj)*epsilon*delta*N*M;
end
end
out = "Happy end";
end
  3 Comments
Matthew Kehoe
Matthew Kehoe on 24 Jul 2021
Thanks for the helpful information (and comments). To address the comments -
First Comment: All of my Matlab code creates dimensions of order (Nx,N+1,M+1). This is arbitary and has no meaning. I consistently create for loops where Nx (the largest value) is before N and M which are smaller. A local test
M = 20;
N = 20;
Nx = 32;
f_n_m = rand(N+1,M+1,Nx);
f_n_m_2 = rand(Nx,N+1,M+1);
ntests = 10000;
% Method 1
tic
for ii = 1:ntests
for j=1:Nx
j_n_m = f_n_m(:,:,j);
end
end
toc
% Method 2
tic
for ii = 1:ntests
for j=1:Nx
j_n_m_2 = f_n_m_2(j,:,:);
end
end
toc
% Method 1 is faster
shows that Method 1 is faster. I should review all of my code and update the double/triple for loops so that the loop goes through the column last (I think this is how column-major works. I will look further into this tomorrow as it is 1:00am here and I need to sleep).
Second Comment: I agree that rand()/randi() is inefficient. It isn't part of my production code and was only created for putting code in the forum. My real code is thousands of lines long (with multiple functions). I don't create data through rand().
Third Comment: There is no need to create coefficient. Writing
for j=1:Nx
if(SumType==1)
% This is what my production code calls. I didn't show what
% taylor_sum2, pade_sum2, pasde_sum2_safe do as that would be too much
% detail and would make the question "not self-contained."
f(j) = taylorsum_2_coeff(f_n_m(:,:,j),Eps,delta,N,M);
elseif(SumType==2)
f(j) = padesum2(f_n_m(:,:,j),Eps,delta,N,M);
else
f(j) = padesum2_safe(f_n_m(:,:,j),Eps,delta,N,M);
end
end
is faster. I will review (and test locally) changing (Nx,N+1,M+1) to (N+1,M+1,Nx) as my production code has a large amount of for loops that loop through something similar to
for j=1:Nx
for n=1:N
for m=1:M
% Do a bunch of stuff and evenually get to
f_n_m = (j,n+1,m+1);
end
end
end
or
for j=1:Nx
f_n_m = (j,:,:);
end
which may be inefficient as the index j should go in the third column. It is interesting to observe that in this setup
M = 20;
N = 20;
Nx = 32;
Gnm = rand(N+1,M+1,Nx);
Gnm_2 = rand(Nx,N+1,M+1);
ntests = 10000;
% Method 1
tic
for ii = 1:ntests
for n=0:N
for m=0:M
f_n_m = Gnm(n+1,m+1,:);
end
end
end
toc
% Method 2
tic
for ii = 1:ntests
for n=0:N
for m=0:M
f_n_m_2 = Gnm_2(:,n+1,m+1);
end
end
end
toc
% Method 2 is faster even though it is of size (Nx,N+1,M+1)

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More Answers (1)

Simon Chan
Simon Chan on 23 Jul 2021
Edited: Simon Chan on 23 Jul 2021
The loop of finding the coefficient can be entirely replaced by:
new_coeff = permute(f_n_m,[2 3 1]);
Noticed that size of new_coeff is 31x31x32 and new_coeff(:,:,1) is equivalent to your coeff when j = 1.

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