# MATLAB Matrix Multiply Efficiency

I am using MATLAB to prototype a few matrix multiply techniques and compare efficiency. Eventually, I will move the prototype codes to C. It is for a homework assignment where we need to write an efficient matrix multiply routine (by being aware of cache size, locality, etc.).

I am curious about the efficiency differences between these two very similar loops:

Matrix Multiply Loop 1 - sum over columns of A times elements of B -> column of C

function [C] = dgemm_naivepe( A,B,C,n )

for j=1:n
tempcol=zeros(n,1);
for k=1:n
for i=1:n
tempcol(i)=tempcol(i)+A(i+(k-1)*n)*B(k+(j-1)*n);
end
end
for k=1:n
C(k+(j-1)*n)=tempcol(k);
end
end


end

Matrix Multiply Loop 2 - sum over columns of A times elements of B -> column of C

function [C] = dgemm_naivepe( A,B,C,n )

for j=1:n
for k=1:n
for i=1:n
C(i+(j-1)*n)=C(i+(j-1)*n)+A(i+(k-1)*n)*B(k+(j-1)*n);
end
end
end

end


After several test runs of various matrix sizes, I found that Loop 1 is faster than Loop 2. Could someone help me understand why this is?

PS: I posted this on a general coding stack exchange but didn't get much of a response, so I figured I could post it here as well.

• I wouldn't trust timings that come out of MATLAB. There's many other things involved that make its timings not indicative of well-optimized C code. Feb 8, 2018 at 4:50

Your loop 2 does not preallocate $C$. Hence it gets resized $n$ times, which requires many reallocations. Try adding C = zeros(n, n) before the for loops.