Struggling a bit with writing a function and passing it to the GPU with via arrayfun

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PetterS 2015-3-19

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评论： Edric Ellis 2015-3-20

I have a long equation that I am currently looping though element by element and its taking forever so I want to make it faster. I’ve read that you can use arrayfun to apply a function to each element using the GPU which could sometimes decrease the computational time a lot. Not sure that method is applicable in all situations (like this one for example), but since I have the parallel computing toolbox I thought I would try it, but I’m not getting it to work, maybe I’m writing the function In the wrong way, I’m trying to understand this but I’m still stuck a bit in my old mindset of working with nested loops which Im trying to get away from.

This is the function file I have created:

 function [o1] = IncAngle(DailyDeclination,DailyHourAngle,Azimuth,Tilt)
o1=acosd(cosd(DailyDeclination)*sind(DailyHourAngle)*sind(Tilt)*sind(Azimuth)+cosd(DailyDeclination)*cosd(DailyHourAngle)*sind(57.6)*sind(Tilt)*cosd(Azimuth)-sind(DailyDeclination)*cosd(57.6)*sind(Tilt)*cosd(Azimuth)+cosd(DailyDeclination)*cosd(DailyHourAngle)*cosd(57.6)*cosd(Tilt)+sind(DailyDeclination)*sind(57.6)*cosd(Tilt));

As you can see it’s a bit long and complicated, but the outcome depends on four variables:

DailyDeclination          % a 1x1440, each value representing one minute of a day
DailyHourAngle            % a 1x1440, each value representing one minute of a day
Azimuth=(-179:2:180)';    % a 180x1, each value representing one orientation on a compass
Tilt=(0:1:180)';          % a 181x1 , each value representing one degree tilt of a surface

And I want the output to be stored as 1440x181x180 representing all minutes during the day for all combinations of tilts and orientations. This is how I try to pass the function to the GPU which I am basing on something similar I saw in an example:

DailyDeclination = gpuArray(DailyDeclination); 
DailyHourAngle = gpuArray(DailyHourAngle); 
Azimuth = gpuArray(Azimuth); 
Tilt = gpuArray(Tilt); 
[o1]=arrayfun(@IncAngle,DailyDeclination,DailyHourAngle,Azimuth,Tilt)

But it only gives me the error: Error using gpuArray/arrayfun Non-singleton dimensions of input arrays must match each other.

I suspect that the error here isn’t just because I’m sending it to the GPU, but that I’m not writing the function input/outputs correctly. How do I specify for the function in which order the elements should be combined and how I want my output to look without using for loops?

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Answer 1

Edric Ellis 2015-3-19

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https://ww2.mathworks.cn/matlabcentral/answers/183994-struggling-a-bit-with-writing-a-function-and-passing-it-to-the-gpu-with-via-arrayfun#answer_171941

在 MATLAB Online 中打开

To use arrayfun in this way, you need each of your vectors to define a different dimension. Something like this simplified example:

% a column vector
v1 = reshape(gpuArray.colon(1, 3), [], 1);
% a row vector
v2 = gpuArray.colon(1, 4);
% 3rd-dimension vector
v3 = reshape(gpuArray.colon(1, 5), 1, 1, []);
% add together elements
arrayfun(@(x,y,z) x + y + z, v1, v2, v3)

In your case, you'll need transpose DailyDeclination and DailyHourAngle to be columns, Tilt needs to be a row (so don't transpose that), and you need to reshape Azimuth to be a 3rd-dimension vector.

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PetterS 2015-3-19

Ok that worked and I simply cannot believe what I am seeing! I used to leave this calculation on overnight and in the morning about 6 of them would be done. With this method it took somewhere around 1-2 seconds to complete. Seriously. Thank you!

So this obviously changed my life forever, but I did find one thing that’s a bit odd: if I calculate it this way and store in say a matrix A and then have another matrix B of the same thing that I calculated the slow way and then type A==B to see if they are identical, about every third value or so is 0 (and the rest are 1). They have the same max, min and mean value but are not 100% identical, is the GPU approximating values differently or why are the results not exactly the same?

Edric Ellis 2015-3-20

Yes, the precise double-precision arithmetic on the GPU doesn't always exactly match the CPU - especially when using trig functions etc. The results should be close to within a few eps though depending on how many operations are involved.

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