How to run the following function on GPU or make it Faster

Question

Med Future 2023-6-19

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1984969-how-to-run-the-following-function-on-gpu-or-make-it-faster

评论： Med Future 2023-6-19

Dataforfasting.mat

I have following MATLAB function It is very slow it takes so much time,it takes 5 seconds to run, but i want to run it in miliseconds,

Can anyone Help me running this code on GPU. I have also Attached the Dataset Below

[valueestimationimage ] = Parameterestimate(Batchdata)
fig=figure; set(fig,'visible','off');
h=histogram(Batchdata,10000,"BinMethod","sturges",'BinWidth',1,'BinLimits',[1 10000]);
sumofbins=max(h.Values);
% size_MP=round(10/100*sumofbins);
size_MP=round(10/100*sumofbins);
ValueofHistogram= h.Values;
Bindata=h.Data;
Binedges=h.BinEdges;
Binedges(end) = Inf;
deleted_data_idx = false(size(Bindata));
for i=1: length(ValueofHistogram)
    if ValueofHistogram(i)<size_MP;
        deleted_data_idx(Bindata >= Binedges(i) & Bindata < Binedges(i+1)) = true;
    end
end
close(fig);
Bindata(deleted_data_idx) = [];
fig=figure; set(fig,'visible','off');
Freq_Data = Bindata;
h = histogram(Freq_Data, 10000, "BinMethod", "sturges", 'BinWidth', 1, 'BinLimits', [1 10000]);
[N, Edges, Bin] = histcounts(Freq_Data, 10000, "BinMethod", "sturges", 'BinWidth', 1, 'BinLimits', [1 10000]);
Retain = N > max(N) / 3.5;
% Find the bin indices that satisfy the condition
FindBins = find(Retain);
% Update RetainDataLv based on the valid bin indices
RetainDataLv = ismember(Bin, FindBins);
% Apply the logical indexing to retrieve the corresponding data
Bindata = Freq_Data(RetainDataLv);
close(fig);
Bindata=round(Bindata).';
[GC, GR] = groupcounts(Bindata) ;
countThresh =30 ; % change this untill you see that the data is fully denoised
denoisedData = Bindata(ismember(Bindata, GR(GC>countThresh))) ; 
% incomingdata= denoisedData.';
if isempty(denoisedData)
incomingdata=Bindata.';
else 
 incomingdata=denoisedData.';
end
[row, column] = size(incomingdata);
for eachrow=1:row
        if column>=1000
%             buffered(eachrow,:) = buffer(incomingdata, 1000);
                groupsize = 1000;
        sig = incomingdata(:);
        if isempty(sig)
            error('signal is empty, cannot buffer it');
        end
        sigsize = numel(sig);
        fullcopies = floor(groupsize ./ sigsize);
        sig = repmat(sig, 1+fullcopies, 1);
        sigsize = numel(sig);
        leftover = mod(sigsize, groupsize);
        if leftover ~= 0
            sig = [sig; sig(1:groupsize-leftover)];
        end
        buffered = buffer(sig, groupsize);
        
        else
         targetsize = 1000;
        sizeofincomingdata = column;
        nrep = targetsize / sizeofincomingdata;
        fullrep = floor(nrep);
        leftover = targetsize - fullrep * sizeofincomingdata;
        buffered=[repmat(incomingdata(eachrow,:), 1, fullrep), incomingdata(1:leftover)];
         sig=buffered.';
        end
end
signal=sig.';
[numImages, lenImage] = size( signal);
imbg = false(10000,lenImage);                                  % background "color"
imfg = ~imbg(1,1);                                  % forground "color"
imSizeOut=[10000 lenImage];        
% ImageSize
for k= 1:numImages        
    imData = round( signal(k,:));  % get pattern
    
    [~,Y] = meshgrid(1:lenImage,1:10000); % make a grid
    
    % black and white image
    BW = imbg;
    BW(Y==imData)=imfg;
    valueestimation=imbinarize(imresize(uint8(BW),imSizeOut));
    % convert to uint8 (0 255)
    valueestimationimage = im2uint8(valueestimation);  
    % resize (from 1000x1000)
    SE=strel('disk',2);
    BW=imdilate(BW,SE);
    
    BW=imbinarize(imresize(uint8(BW),imSizeOut));
    % convert to uint8 (0 255)
    imoriginalestimate = im2uint8(BW);
   
end
end