parFor Worker unable to find file. Unrecognized function or variable 'parameters'

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CSCh 2021-8-13

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/897507-parfor-worker-unable-to-find-file-unrecognized-function-or-variable-parameters

评论： CSCh 2021-8-25

在 MATLAB Online 中打开

Hi there I have trouble to paralize the the loop.

It is from here:

Solve Partial Differential Equations Using Deep Learning - MATLAB & Simulink - MathWorks Schweiz

if I try to run the code below, parfor complains about the "iteration". If I set the iteration=epch, the the follwowing error occurs:

The source code ("XX".m) for the parfor-loop that is trying to execute on the worker could not be found.

Caused by:

Unrecognized function or variable 'parameters'.

Error using remoteParallelFunction (line 84)

Worker unable to find file.

Unrecognized function or variable 'parameters'.

But the "parameters" are defined-as you can see- inside the link above.

Any idea is welcome.!

Chris

start = tic;
iteration = 0;
parpool(32);
parfor epoch = 1:numEpochs
    reset(mbq);
    while hasdata(mbq)
        iteration = iteration + 1;
        dlXT = next(mbq);
        dlX = dlXT(1,:);
        dlT = dlXT(2,:);
        % Evaluate the model gradients and loss using dlfeval and the
        % modelGradients function.
        [gradients,loss] = dlfeval(accfun,parameters,dlX,dlT,dlX0,dlT0,dlU0);
        % Update learning rate.
        learningRate = initialLearnRate / (1+decayRate*iteration);
        % Update the network parameters using the adamupdate function.
        [parameters,averageGrad,averageSqGrad] = adamupdate(parameters,gradients,averageGrad, ...
            averageSqGrad,iteration,learningRate);
    end
    % Plot training progress.
    loss = double(gather(extractdata(loss)));
    addpoints(lineLoss,iteration, loss);
    D = duration(0,0,toc(start),'Format','hh:mm:ss');
    title("Epoch: " + epoch + ", Elapsed: " + string(D) + ", Loss: " + loss)
    drawnow
end

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Edric Ellis 2021-8-13

It doesn't look like you've defined parameters inside the parfor loop? The first reference I see is in the dlfeval call. To be a legal parfor loop, you'd need to define parameters just after the start of each iteration of parfor.

I suspect however that this loop isn't parallelisable in this way. Surely each iteration of the loop depends on the previous iteration (via the parameters variable).

CSCh 2021-8-13

Thank you for your comment Edric,

If this loop is not parallelisable, are there any workarounds available?

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Answer 1

Raymond Norris 2021-8-13

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As Edric mentioned, this is/may not parallizable. But if it were, you'd need to make modifications to the for-loop. You've included the updating of the figure within the parfor, which won't work as you're expecting (the pool of workers don't have access to your client MATLAB displaying the plot). There are ways around this, but won't address the fundemental issue of whether the loop can be parallelized.

I would suggest circling back to the Training Options section of the example, where it describes using GPUs to help train the model. I suspect this is where you find you best option to speed up the code.

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Walter Roberson 2021-8-16

在 MATLAB Online 中打开

Your parfor version has

parfor epoch = 1:numEpochs

which looks like it was derived from a non-parallel version,

for epoch = 1:numEpochs

Now... suppose you were to replace that with

for epoch = randperm(numEpochs)

so that the epoch were only done one at a time, but they were done in a random order. So instead of the parameters for epoch 2 being whatever was estimated for epoch 1, the parameters for epoch 2 might be what was estimated after doing epochs 17, 4, 38, 5 (in that order.... as an example.) The results that would be produced would certainly be different than proceeding in sequence. Would the results be acceptable though?

Now, suppose you were able to do two epoch simultaneously -- say epoch = 1 and epoch = 2. The parameters would be the same for both, so instead of the parameters for epoch 2 being informed by the results for epoch 1, both epoch 1 and 2 would use the same initial parameters. Suppose that epoch 1 and epoch 2 take very nearly the same amount of time... epoch 1 might finish first, or epoch 2 might finish first, When one of them finished and you went on to epoch 3, would it be acceptable that the parameters would be set according to whichever one of the two had finished most recently? So due to accidents of timing, epoch 3 might use the parameters generated from epoch 1, or possibly epoch 3 might use the parameters generated from epoch 2 (depending which finished first), and epoch 4 might end up using the estimates from the other one of the two... but that due to accidents of timing, epoch 3 might not be ready to go until both epoch 1 and 2 were finished, so epoch 3 and epoch 4 might end up both using whichever of epoch 1 and epoch 2 finished last. The results would be different than proceeding one at a time in sequence, and the results would not be repeatable, but would the resutls be acceptable ??

If the results would be acceptable, then we could probably work something out for you.

Walter Roberson 2021-8-25

Could you remind us if your computation is single precision or double precision?

512 cores looks to be one of:

GeForce GTX 580 (late 2010) (GF110 -- Fermi based), which does double precision at 1/8 of the single precision rate, about 197 gigaflops
GeForce GTX 750 (early 2014) (GM107 -- Maxwell based), which does double precision at 1/32 of the single precision rate, about 34 gigaflops

You may have noticed that the older card does double precision about 6 times faster than the newer card. When you are doing double-precision work you need to pay a lot of attention to the specifications for the individual model !!

CSCh 2021-8-25

I think it is single.

I use a NVIDIA® QUADRO® P620

Datasheet Quadro P620 (nvidia.com)

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