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Estimating the execution time of models in Engee

In this example, we will analyze the possibilities of comparing models by speed.

To demonstrate, let's take two identical models with identical parameters. There is one difference: in the first case, pipeline calculations will be used, and in the second, vector calculations.

The models themselves are shown in the figure below.

image_4.png image_5.png

Function for launching the model

In [ ]: 
Pkg.add(["TickTock"])
In [ ]: 
# Подключение вспомогательной функции запуска модели.
function run_model( name_model)
    
    Path = (@__DIR__) * "/" * name_model * ".engee"
    
    if name_model in [m.name for m in engee.get_all_models()] # Проверка условия загрузки модели в ядро
        model = engee.open( name_model ) # Открыть модель
        model_output = engee.run( model, verbose=true ); # Запустить модель
    else
        model = engee.load( Path, force=true ) # Загрузить модель
        model_output = engee.run( model, verbose=true ); # Запустить модель
        engee.close( name_model, force=true ); # Закрыть модель
    end
    sleep(5)
    return model_output
end
Out[0]:
run_model (generic function with 1 method)

Comparison of work speed

Now let's move on to comparing the speed of work. Let's use the TickTock library and tick() functions. and tok()

tick() — records the current time. It is usually used to start measuring time.
tok() — calculates and outputs the time elapsed since the last tick() call.

Features and benefits of using TickTock:

  1. Functions of tick() and tok() Provide a simple and intuitive way to measure time without having to manually calculate the difference between start and end times.
  2. TickTock works with high time accuracy, which allows you to get reliable data on the duration of code execution, even for very fast operations.
  3. The measurement result is displayed in an easy-to-read format (seconds, milliseconds), as well as the standard Float64 output data format, which makes it easy to convert these values to minutes or hours.
In [ ]: 
using TickTock
In [ ]: 
tick()
run_model("sin_Sample") # Запуск модели.
Ts = tok()
Out[0]:
7.545560097
In [ ]: 
tick()
run_model("sin_Frame") # Запуск модели.
Tf = tok()
Out[0]:
7.903039812
In [ ]: 
println("Time sin_Sample: $(Ts)")
println("Time sin_Frame: $(Tf)")
println("Time difference: $(abs(Ts-Tf))")

Time sin_Sample: 7.545560097
Time sin_Frame: 7.903039812
Time difference: 0.3574797150000002

As we can see from the calculation of the difference, the execution time differs slightly. Let's also compare the simulation results in order to make sure that the models are identical.

In [ ]: 
Ss = collect(Ss);
Sf = collect(Sf);
Sf = reduce(vcat, Sf.value)

println("Size sin_Sample: $(size(Ss))")
println("Size sin_Frame: $(size(Ss))")

Size sin_Sample: (10001, 2)
Size sin_Frame: (10001, 2)

As we can see, the dimensions of the signals are equal.

In [ ]: 
gr()
error = abs.(Sf[1:100]-Ss.value[1:100]);

plot(Ss.value[1:100], label="sin_Sample")
plot!(Sf[1:100], label="sin_Frame")
plot!(error, label="difference")
title!("sum_error: $(sum(error))")
Out[0]:

The signals are identical both in size and in comparison of the signals themselves.

Conclusion

In this example, we looked at how the TickTock library can be used to compare the speed of different models. This tool is useful for analyzing the performance of your models and evaluating the need to optimize them.

Blocks used in example