the utilization of cpu and gpu is low, how to increase them?

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while i am using matlab to train a alexnet from the scratch on window, the utilization ratio of cpu and gpu of my computer is low, and I wonder how to increase them.
here is my code:
trainImagesetPath = 'E:\deep_learning_dataset\tiny-imagenet-200\train';
valImagesetPath = 'E:\deep_learning_dataset\tiny-imagenet-200\val';
testImagesetPath = 'E:\deep_learning_dataset\tiny-imagenet-200\test';
miniBatchSize = 960;
imdsTrain = imageDatastore(trainImagesetPath, 'IncludeSubfolders', true, ...
'LabelSource', 'foldernames', 'FileExtensions',{'.jpg','.JPG', '.JPEG'}, 'ReadSize', miniBatchSize);
imdsValidation = imageDatastore(valImagesetPath, 'IncludeSubfolders', true, ...
'LabelSource', 'foldernames', 'FileExtensions',{'.jpg','.JPG', '.JPEG'}, 'ReadSize', miniBatchSize);
imdsTest = imageDatastore(testImagesetPath, 'IncludeSubfolders', true);
layers = [imageInputLayer([224 224 3])
convolution2dLayer(11, 96, 'Stride', [4, 4], 'Padding', [0 0 0 0])
reluLayer
batchNormalizationLayer
maxPooling2dLayer(3, 'Stride', [2, 2], 'Padding', [0 0 0 0])
groupedConvolution2dLayer(5, 128, 2, 'Stride', [1, 1], 'Padding', [2 2 2 2])
reluLayer
batchNormalizationLayer
maxPooling2dLayer(3, 'Stride', [2, 2], 'Padding', [0 0 0 0])
convolution2dLayer(3, 384, 'Stride', [1, 1], 'Padding', [1 1 1 1])
reluLayer
groupedConvolution2dLayer(3, 192, 2, 'Stride', [1, 1], 'Padding', [1 1 1 1])
reluLayer
groupedConvolution2dLayer(3, 128, 2, 'Stride', [1, 1], 'Padding', [1 1 1 1])
reluLayer
maxPooling2dLayer(3, 'Stride', [2, 2], 'Padding', [0 0 0 0])
fullyConnectedLayer(4096)
reluLayer
dropoutLayer(0.5)
fullyConnectedLayer(4096)
reluLayer
dropoutLayer(0.5)
fullyConnectedLayer(200)
softmaxLayer
classificationLayer
];
% analyzeNetwork(layers);
inputSize = [224, 224, 3];
augimdsTrain = augmentedImageDatastore(inputSize, imdsTrain, 'ColorPreprocessing', 'gray2rgb', 'DispatchInBackground', true);
augimdsValidation = augmentedImageDatastore(inputSize, imdsValidation, 'ColorPreprocessing', 'gray2rgb', 'DispatchInBackground', true);
augimdsTest = augmentedImageDatastore(inputSize, imdsTest, 'ColorPreprocessing', 'gray2rgb');
options = trainingOptions('adam', ...
'MiniBatchSize', miniBatchSize, ...
'MaxEpochs',120, ...
'InitialLearnRate',1e-4, ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.25, ...
'LearnRateDropPeriod', 5, ...
'DispatchInBackground', true, ...
'Shuffle','every-epoch', ...
'ValidationData', augimdsValidation, ...
'ValidationFrequency', 20, ...
'Verbose',true, ...
'Plots','training-progress', ...
'ExecutionEnvironment', 'auto');
tic
alexNetModel = trainNetwork(augimdsTrain,layers,options);
fprintf('training process time cost: ');
toc
[YPred,scores] = classify(alexNetModel,augimdsTest);
YTest = imdsTest.Labels;
accuracy = mean(YPred == YTest);
fprintf('test acc: %f\n', accuracy);
figure
confusionchart(YTest, YPred)
my computer is a lenovo laptop called r9000k 2021 with rtx3080 laptop GPU, and the utilization ratio is shown as follow:

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R2021a

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