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Optimization of loops with deep learning functions

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Radians
Radians on 22 Jun 2021
Commented: Radians on 28 Jun 2021
Hello,
I am trying to optimize the following code, which performs the deep learning convolutions on input arrays:
parfor k=1:500 %number of images
for j =1:8 %number of channels of the relevant filters
dldf_O_dlconv3_temp_1(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,1,k),[8 8 1]),'SSC'),0,'Padding',padding);%1st filter
dldf_O_dlconv3_temp_2(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,2,k),[8 8 1]),'SSC'),0,'Padding',padding);%2nd filter
dldf_O_dlconv3_temp_3(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,3,k),[8 8 1]),'SSC'),0,'Padding',padding);%3rd filter
dldf_O_dlconv3_temp_4(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,4,k),[8 8 1]),'SSC'),0,'Padding',padding);%4th filter
dldf_O_dlconv3_temp_5(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,5,k),[8 8 1]),'SSC'),0,'Padding',padding);%5th filter
dldf_O_dlconv3_temp_6(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,6,k),[8 8 1]),'SSC'),0,'Padding',padding);%6th filter
dldf_O_dlconv3_temp_7(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,7,k),[8 8 1]),'SSC'),0,'Padding',padding);%7th filter
dldf_O_dlconv3_temp_8(:,:,j,k)=dlconv(dlarray(reshape(O_maxpool2(:,:,j,k),[8 8 1]), 'SSC'),dlarray(reshape(DLDO_O_dlconv3(:,:,8,k),[8 8 1]),'SSC'),0,'Padding',padding);%8th filter
end
end
As you can see, I am already using the parfor loop. I tried using GPU arrays with O_maxpool2 and DLDO_O_dlconv3, but instead of speeding anything up I think it became a bit slower if anything. My GPU device details are as follows:
CUDADevice with properties:
Name: 'GeForce RTX 2080 Ti'
Index: 1
ComputeCapability: '7.5'
SupportsDouble: 1
DriverVersion: 11.2000
ToolkitVersion: 11
MaxThreadsPerBlock: 1024
MaxShmemPerBlock: 49152
MaxThreadBlockSize: [1024 1024 64]
MaxGridSize: [2.1475e+09 65535 65535]
SIMDWidth: 32
TotalMemory: 1.1811e+10
AvailableMemory: 9.4411e+09
MultiprocessorCount: 68
ClockRateKHz: 1545000
ComputeMode: 'Default'
GPUOverlapsTransfers: 1
KernelExecutionTimeout: 1
CanMapHostMemory: 1
DeviceSupported: 1
DeviceAvailable: 1
DeviceSelected: 1
Please let me know if there is anything else I could do to speed this code, and also why using gpuArrays have not sped it up.
Much thanks.

Accepted Answer

Joss Knight
Joss Knight on 24 Jun 2021
Edited: Joss Knight on 24 Jun 2021
dlconv is designed to work in batch with multiple input channels, multiple filters, and multiple input observations in a single call. Read the documentation for dlconv.
  1 Comment
Radians
Radians on 28 Jun 2021
Thanks alot...Even though it was not straight forward what I was trying to do(that's why I had to use forloops), but your comment made me think about how I could utilize the built-in functionality of dlconv to remove the loops, and after reshaping my data a bit I was able to do it. Thanks again.

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