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Say I have many (around 1000) large matrices (about 1000 by 1000) and I want to add them together element-wise. The very naive way is using a temp variable and accumulates in a loop. For example,

summ=0;

for ii=1:20

for jj=1:20

summ=summ+ rand(400);

end

end

After searching on the Internet for some while, someone suggests it's better to do with the help of sum(). For example,

sump=zeros(400,400,400);

count=0;

for ii=1:20

for j=1:20

count=count+1;

sump(:,:,count)=rand(400);

end

end

summ=sum(sump,3);

However, after I tested two ways, the result is

Elapsed time is 0.780819 seconds.

Elapsed time is 1.085279 seconds.

which means the second method is even worse.

So I am just wondering if there any effective way to do addition? Assume that I am working on a computer with very large memory and a GTX 1080 (CUDA might be helpful but I don't know whether it's worthy to do so since communication also takes time.)

Thanks for your time! Any reply will be highly appreciated!.

Jan
on 8 May 2018

Edited: Jan
on 4 Nov 2019

The main time is spent in rand() in your example. With using ones() instead, the runtime goes from 0.71 sec to 0.25 sec on my machine.

Instead of creating the matrices explicitely, you could think of solving the problem mathematically, if the matrices are really exp(i*x+j*y). So please post the real code, not just some dummy code, whose most expensive function is not part of the real problem at all.

Jan
on 8 May 2018

A cleaned version of the code would be:

x = linspace(nn(1),pp(1),NN);

y = linspace(nn(2),pp(2),NN);

b11 = b1(1);

b12 = b1(2);

b21 = b2(1);

b22 = b2(2);

s = 0;

for j = -Nmax:Nmax

for k = -Nmax:Nmax

s = s + aa(counter) * ...

exp(1i*((j*b11 + k*b21) * x + ...

(j*b12 + k*b22) * y));

end

end

The most expensive part is the exp function here. As far as I can see, the argument of exp() is a [1 x NN] vector and not a [NN x NN] matrix. Then the code should fail with an error message. Please post a running code. Replace functions like your energy by rand, if it is sufficient for the computations.

It is hard to suggest methods to accelerate code, which does not run at all. But the general idea is to reduce the number of exp evaluations. Use exp(a+b) = exp(a)*exp(b). Instead of creating the matrix as argument based on two vectors, calculate the exp at first and create the matrix afterwards. In addition you might be able to exploit, that x and y are created by linspace:

x = linspace(1, 10, 2000);

e1 = exp(x); % 2000 expensive exp() calls

c = x(2) - x(1);

% 2 expensive exp() calls and a cheaper cumulative product:

e2 = cumprod([exp(x(1)), repmat(exp(c), 1, length(x)-1)]);

The cumulative product is much cheaper, but suffers from the accumulated rounding error.

You can use an equivalent method to use the value of the exp function at k=n to get the value for k=n+1.

I recommend to write down the formula and simplify the equation with paper and pencil at first. Solving the sum by brute computing power is less efficient.

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