Hi, I open this topic because I found that sometimes Flux can use 100% of the CPU, but sometimes it only uses half.

For example, here is two demo model, model A have fewer hidden layers than B. Trainning model A, the CPU% is alway 100%, but in model B, it drop down to about 50%,

```
using Flux
demodata = rand(Float32,10000,128)
model_A = Chain(
Dense(10000 => 64, relu),
Dense(64 => 10000,relu))
model_B = Chain(
Dense(10000 => 512, relu),
Dense(512 => 218,relu),
Dense(218 => 64,relu),
Dense(64 => 218,relu),
Dense(218 => 512,relu),
Dense(512 => 10000,relu))
myLoss(Model,X) = begin
Flux.Losses.mse(Model(X),X)
end
# training model_A
ps = Flux.params(model_A);
for i in 1:100
Flux.train!(myLoss, ps,[(model_A, demodata)], ADAM() )
end
# training model_B
ps = Flux.params(model_B);
for i in 1:100
Flux.train!(myLoss, ps,[(model_B, demodata)], ADAM() )
end
```

here is the CPU% when the model A is runing

and this one is the model B 's

I am not an expert in this area, but according to my understanding, model B requires more computational power, so, what limits it to using only 50% CPU? and any suggestion how to improve the CPU% in model B training?

Thank you for your patience in reading

I also found this topic related to Flux and CPU, but it is more about multi-threads support.