How can I improve this really slow code, which consists of several nested for loops?

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Hi,
So I've written these nested for loops, but it takes a very long time to execute this code:
for T = linspace( 1, 5, 10 )
for xdot_0 = linspace( -3, 3, 10 )
for ydot_0 = linspace( 16, 20, 10 )
for thetadot_0 = linspace( -5, 0, 10 )
z = DifferenceMap( xdot_0, ydot_0, thetadot_0, T );
if norm(z) < 1e-3
disp( xdot_0, ydot_0, thetadot_0, T )
disp( z )
end
end
end
end
end
The DifferenceMap( ) function calls another function that gives solutions from the ode45 solver at final time, T.
Is there a better way to write this code so that it's faster?
Thanks,
  4 个评论
Noob
Noob 2020-9-29
编辑:Noob 2020-9-29
Hi Sindar,
Should I do both -- use a parfor and look into paralleizing DifferenceMap -- or are there "diminishing returns", so that doing one of the two is the best I can hope for? (In any case, I'll start reading up on both suggestions.)
Thanks,
Sindar
Sindar 2020-9-29
There are definitely diminishing returns. parfor is easy to implement, but if you figure out lower-level parallelization (esp. vectorization), it has a good chance to be more effective

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Walter Roberson
Walter Roberson 2020-9-29
If you can, arrange DifferenceMap to accept a vector of T values. In the ode45() call, instead of passing in the two-element vector [0 T] for scalar T, pass in [0 vector_of_T] . ode45() will report back results at only those times. Discard the first row (corresponding to T = 0) and the rest of the rows hold the results required for each of the values in the vector of T values.
At present, your code is evaluating the same system of ode equations for a number of different T values, but at quite different times -- you do not do T = 1.4 until you have finished all of the T = 1 cases. But in order to simulate to T = 1.4, ode45 would have to integrate starting from scratch, from 0, re-doing the integrations done for time 0 to 1. And then eventually T would become 1.9 and ode45 would have to integrate the same system from scratch again, going all the way through starting from 0 to 1.4 .
My proposal is to do all the times in the same call for a given configuration, so that within the same call after making the prediction for T = 1, it would be able to use the data it already has in order to move on to T = 1.4, and through to T = 1.9 and so on, not needing to re-do the [0 1], [0 1.4] and so on integrations.
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