funGPU

 For testing the GPU capabilities on Matlab with testGPU.m script
 Y = exp(X) [by the Horner's rule]  + sinc(X^2)
 Z is based on matrix product for testing the effect of network between cores on GPU calculations

   INPUTS:

       - X: any tensor [n,m,p,....]
       - a: scalar or tensor [n,m,p,....]
       - b: scalar or tensor [n,m,p,....]
       - c: parameters in cell format [1,3]
       - d: parameters in structure format [1,3]

   OUTPUTS:
   
       - Y: processed tensor [n,m,p,....]
       - Z: processed tensor [n,m,p,....]
       - T: processed structure T.y = Y and T.z = Z [structure]
       - W: processed cell W{Y}, W{Z} [table of cells]

 by Yves Peysson <yves.peysson@cea.fr> (CEA-IRFM) and Joan Decker <joan.decker@cea.fr> (CEA-IRFM)

0001 function [Y,Z,T,W] = funGPU(X,a,b,c,d) 
0002 %
0003 % For testing the GPU capabilities on Matlab with testGPU.m script
0004 % Y = exp(X) [by the Horner's rule]  + sinc(X^2)
0005 % Z is based on matrix product for testing the effect of network between cores on GPU calculations
0006 %
0007 %   INPUTS:
0008 %
0009 %       - X: any tensor [n,m,p,....]
0010 %       - a: scalar or tensor [n,m,p,....]
0011 %       - b: scalar or tensor [n,m,p,....]
0012 %       - c: parameters in cell format [1,3]
0013 %       - d: parameters in structure format [1,3]
0014 %
0015 %   OUTPUTS:
0016 %
0017 %       - Y: processed tensor [n,m,p,....]
0018 %       - Z: processed tensor [n,m,p,....]
0019 %       - T: processed structure T.y = Y and T.z = Z [structure]
0020 %       - W: processed cell W{Y}, W{Z} [table of cells]
0021 %
0022 % by Yves Peysson <yves.peysson@cea.fr> (CEA-IRFM) and Joan Decker <joan.decker@cea.fr> (CEA-IRFM)
0023 %
0024 if nargin == 3,
0025     [Y,Z] = funGPU1(X,a,b);
0026 elseif nargin == 4,%matrix product with cell (imaginary or real)
0027     if ~isempty(c) && iscell(c),
0028         [Y,Z] = funGPU1(X,a,b,c); 
0029     else
0030         [Y,Z] = funGPU1(X,a,b);    
0031     end
0032 elseif nargin == 5,%matrix product with structure (imaginary or real)
0033     if ~isempty(c) && iscell(c) && isstruct(d),
0034         %
0035         [Y,Z] = funGPU1(X,a,b,c);       
0036         %
0037         fn = fieldnames(d);
0038         for id = 1:size(d),
0039             for ifn = 1:length(fn),
0040                 if ~isempty(d(id).(fn{ifn})),
0041                     Z = d(id).(fn{ifn}).*Z^2;
0042                 end
0043             end
0044         end
0045     else
0046         [Y,Z] = funGPU1(X,a,b);
0047     end
0048 end
0049 %
0050 T.y = Y;
0051 T.z = Z;
0052 %
0053 W{1} = Y;
0054 W{2} = Z;
0055 %
0056 end
0057 %
0058 function [Y,Z] = funGPU1(X,a,b,c)
0059     %
0060     Y = 1 + X.*(1 + X.*((1 + X.*((1 + X.*((1 + X.*((1 + X.*((1 + X.*((1 + X.*((1 + X./9)./8))./7))./6))./5))./4))./3))./2)) + sinc(X.^2);%matrix dot-product (Horner's rule to compute y = exp(x) + sinc(x^2))
0061     %
0062     if imag(a)~=0 || imag(b)~=0,
0063         Z = a - 3*b + Y*(1 + Y*((1 + Y*((1 + Y*((1 + Y*((1 + Y*((1 + Y*((1 + Y*((1 + Y/9)/8))/7))/6))/5))/4))/3))/2));%for testing matrix product that uses heavily network between cores
0064     else
0065         Z = '';
0066     end
0067     %
0068     if nargin == 4,
0069         Z = c{1}.*Y - c{2}.*Y.^2 + c{3}.*Y.^3;
0070     end
0071 end