Hi - please forgive the naivety of this question, I am just getting familiar with GPU computing and am working through this slowly.
I have a system, that takes in an Nx1 vector of states, and an objective function that performs many arithmetic operations to output an Nx1 vector of outputs. I would like to repeat this for a large number of independent vectors representing different objects (say, 100,000). In general, each element of the Nx1 vector depends on others within the vector to produce the output, but does not depend on elements from other vectors. Instead of serial computation with a for loop, is there a good way to parallelize this on the GPU?
To clarify (if needed):
function yprime=compderiv(y)
yprime(1)=alpha*(y(2)-y(1));
yprime(2)=beta*(y(2));
yprime(3)=(y(1)+y(2)+y(3))/gamma;
...
yprime(72)=...
return
for i=1:100000
y=rand(72,1);
yprime(:,i)=compderiv(y);
end
Naively, I thought of passing the elements of my Nx1 vector individually to a function to compute individual outputs, and then using arrayfun, but N is large (>70), and it is cumbersome to pass that many variables to it:
function [yp1,yp2,yp3...
...
return
y1=gpuArray.rand(1,100000);
y2=gpuArray.rand(1,100000);
y3=gpuArray.rand(1,100000);
...
y72=gpuArray.rand(1,100000);
[ypp1,ypp2,ypp3...
Is there a more elegant solution to this kind of problem?
