ownsend23_sda asked . 2021-07-22

Neural networks - CUDAKernel?/setConsta?ntMemory

Neural networks - CUDAKernel?/setConsta?ntMemory - the data supplied is too big for constant 'hintsD'

On R2015a with Parallel Computing Toolbox and Neural Network Toolbox.

Using the following code with GPU Nvidia GeForce GTX980 Ti:

net1 = feedforwardnet(20);

net1.trainFcn = 'trainscg';

x = inputs(1:4284,2:2000)'; % if I reduce this to 2:1900, it will work

t = double(targets'); % casting to double for GPU

t = t(:,1:4284);

% preparing for GPU xg = nndata2gpu(x); tg = nndata2gpu(t);

net1.input.processFcns = {'mapminmax'}; net1.output.processFcns = {'mapminmax'};

net2 = configure(net1,x,t); % Configure with MATLAB arrays

net2 = train(net2,xg,tg);

As you can see, this is not a big dataset. When I run this, it generates this error:

Error using parallel.gpu.CUDAKernel/setConstantMemory The data supplied is too big for constant 'hintsD'.

Error in nnGPU.codeHints (line 33) setConstantMemory(hints.yKernel,'hintsD',hints.double);

Error in nncalc.setup2 (line 13) calcHints = calcMode.codeHints(calcHints);

Error in nncalc.setup (line 17) [calcLib,calcNet] = nncalc.setup2(calcMode,calcNet,calcData,calcHints);

Error in network/train (line 357) [calcLib,calcNet,net,resourceText] = nncalc.setup(calcMode,net,data);

gpuDevice is showing this:

                Name: 'GeForce GTX 980 Ti'

                     Index: 1

         ComputeCapability: '5.2'

            SupportsDouble: 1

             DriverVersion: 8

            ToolkitVersion: 6.5000

        MaxThreadsPerBlock: 1024

          MaxShmemPerBlock: 49152

        MaxThreadBlockSize: [1024 1024 64]

               MaxGridSize: [2.1475e+09 65535 65535]

                 SIMDWidth: 32

               TotalMemory: 6.4425e+09

           AvailableMemory: 5.1520e+09

       MultiprocessorCount: 22

              ClockRateKHz: 1139500

               ComputeMode: 'Default'

      GPUOverlapsTransfers: 1

    KernelExecutionTimeout: 1

          CanMapHostMemory: 1

           DeviceSupported: 1

            DeviceSelected: 1

As noted in the code above, if I reduce x marginally, it will run. I don't understand why data of this size would generate a memory error? Am I missing a step in preparing this for GPU?

neural network , memory , gpu , matlab

Expert Answer

Kshitij Singh answered . 2024-05-17 22:02:19

I was able to reproduce your issue. The best solution is to do the GPU training a different way by using the 'useGPU' flag. This does not use the shared memory in this way, and side-steps this issue. Your example code would look like this:

net1 = feedforwardnet(20);
net1.trainFcn = 'trainscg';

x = inputs(1:4284,2:2000)';

t = double(targets'); % casting to double for GPU

t = t(:,1:4284);

net1.input.processFcns = {'mapminmax'};
net1.output.processFcns = {'mapminmax'};

net1 = train(net1,x,t,'useGPU','yes');

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ownsend23_sda asked . 2021-07-22

Neural networks - CUDAKernel?/setConsta?ntMemory

neural network , memory , gpu , matlab

Expert Answer

Kshitij Singh answered . 2024-05-17 22:02:19

Not satisfied with the answer ?? ASK NOW

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ownsend23_sda asked . 2021-07-22

Neural networks - CUDAKernel?/setConsta?ntMemory

neural network , memory , gpu , matlab

Expert Answer

Kshitij Singh answered . 2024-05-17 22:02:19

Not satisfied with the answer ?? ASK NOW

MOST VIEWED QUESTIONS

Recent Matlab Projects

Classification of Covid and Non-Covid Lungs CT-Scan using Deep Learning with MATLAB

Matlab simulation on Wind Energy system

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