Parallel evaluation and simple statistics

In this example we will be looking at a way to evaluate several times a given problem by exploiting a distributed computing strategy known as embarrasingly parallel execution.

The idea is very simple: compute the same problem several times, in parallel, and collect the results at the end. This should work well with metaheuristics due to the following properties:

• Metaheuristics are self-contained, meaning that they possess their own RNG and their own search space.
• Due to the fact that solutions are searched with a random component, independent runs should be able to give independent results.

With this in mind, we can use the fantastic parallel capabilities of Julia and show how easy it is to obtain some simple, but meaningful statistics for a given optimization problem.

With this we want to also show that this is the right way to use this package. We should always run several independent solvers to obtain good statistics. We must never settle with just one run from the solver.

Setting up

In order to use the multithreading capabilities of Julia, we need to launch the REPL using all the threads we can afford.

For instance, the computer where this example is currently being executed has 8 physical cores, so I should launch my Julia REPL like so

JULIA_NUM_THREADS=8 julia

This will launch the REPL with 8 cores ready to be used. We can check this by entering the following in the REPL,

julia> using Base.Threads
julia> nthreads()

making sure that this matches the same number introduced with the variable JULIA_NUM_THREADS.

You should replace the number of threads, in this case 8, with the number of threads that you wish.

Code set up

First, let us import all the necessary modules we will need for this example.

using BioMetaheuristics
using BioMetaheuristics.TestFunctions
using Base.Threads
using Statistics
using BenchmarkTools

Now, let us create some setup functions. First, we will be using the optimize interface to solve the Levy optimization problem. This function just wraps the optimize function and returns the solution vector from the problem.

We will be using Particle Swarm Optimization (PSO), with 20,000 iterations. It is very important to note that no specific RNG is being used. This tells the optimize function to always use a new, truly randomly seeded RNG.

function solve_problem(dim)
    val = optimize(Levy(), zeros(dim), [-35.0, 35.0], PSO(); iters=20_000)

    return val.x
end;

Some benchmarks

Before we continue, I would like to show a benchmark of the PSO implementation. We will use the fantastic BenchmarkTools.jl to check the time it takes to finish a single optimization problem.

Here, we will solve a 50-dimensional Levy optimization problem. We are not really interested if the solution is accurate, just the timing. We will deal with accuracy in the next subsection.

b = @benchmark solve_problem(50);
b

BenchmarkTools.Trial: 
  memory estimate:  37.09 GiB
  allocs estimate:  184553015
  --------------
  minimum time:     20.916 s (48.39% GC)
  median time:      20.916 s (48.39% GC)
  mean time:        20.916 s (48.39% GC)
  maximum time:     20.916 s (48.39% GC)
  --------------
  samples:          1
  evals/sample:     1

Not bad at all. If it takes between 3-4 seconds to finish I think this is fairly good. Furthermore, it means that we can take advantage of how little it takes to run in order to really push the number of solvers to execute.

Parallel evaluation

We now turn to the main event. We will launch several solvers using the previously created wrapper function in parallel. First off, we need another function to do so.

This function will use the @threads macro that enables a for-loop to run in parallel. The function has a matrix called solutions where we will be storing the solution vectors from each independent run.

We will call the solver in parallel and each time we will save the solutions vector. We will then compute the mean, median and standard deviation from all the independent runs and report that as the true value, and compare it to the ground truth.

function parallel_compute(;dim=50, total_iterations=50)
    solutions = Matrix{Float64}(undef, dim, total_iterations)

    @threads for i in 1:total_iterations
        solutions[:, i] = solve_problem(dim)
    end

    mean_result = mean(solutions; dims=2)
    median_result = median(solutions; dims=2)
    std_result = std(solutions; dims=2)

    return (mean_result, median_result, std_result)
end;

We are ready to go. We will do 200 iterations in order to have good statistics.

μ, med, σ = parallel_compute(;total_iterations=200);

We can now check the results

Here is the mean value

μ'

1×50 LinearAlgebra.Adjoint{Float64,Array{Float64,2}}:
 1.0  1.0  1.0  0.989071  1.0  1.0  1.0  1.0  1.0  0.994534  1.0  1.0  1.0  1.0  0.999999  1.0  1.0  1.0  0.994535  0.994535  1.0  0.994536  1.0  0.999999  1.0  1.0  0.989069  1.0  1.0  0.994535  0.999998  0.98907  0.98907  0.98907  0.994535  1.0  0.994535  1.0  0.994534  1.0  0.994535  1.0  0.999999  0.994535  0.994535  0.999999  1.0  0.994535  1.0  1.0

Here is the median obtained

med'

1×50 LinearAlgebra.Adjoint{Float64,Array{Float64,2}}:
 1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0

And finally, the standard deviation of the solution

σ'

1×50 LinearAlgebra.Adjoint{Float64,Array{Float64,2}}:
 5.85214e-6  8.49717e-6  5.11064e-6  0.109025  8.98645e-6  4.90472e-6  2.93619e-6  1.57354e-5  2.36602e-5  0.0772862  5.76815e-6  7.94483e-6  4.00857e-6  5.4588e-6  7.67163e-6  6.13454e-6  4.39584e-6  5.04256e-6  0.0772863  0.0772865  7.66157e-6  0.0772864  8.72139e-6  1.02898e-5  5.53272e-6  2.56498e-6  0.109024  1.02486e-5  6.3433e-6  0.0772864  1.40778e-5  0.109024  0.109025  0.109024  0.0772864  4.72514e-6  0.0772863  9.02386e-6  0.0772863  6.05015e-6  0.0772863  6.94921e-6  6.67594e-6  0.0772866  0.0772867  7.74873e-6  4.70179e-6  0.0772864  5.12988e-6  5.48866e-6

Let us print the evaluation of the function on these results. First, the mean value.

println(evaluate(Levy(), μ))

0.00047890369305421924

Then, the median obtained.

println(evaluate(Levy(), med))

2.250210231361768e-14

Conclusions

Recall that for the Levy function the solution is

and it should evaluate to

It it safe to say that we have obtained those results, but we are statistically sure that these results are the true results, because we have several independent runs for the answer.

On the use of the median

Notice that most of the time, the median result actually performs better than the mean result. This is somewhat expected, as the median is not really affected by poorly performant runs. Sometimes, independent runs will not give good results and when taken into account, the mean value will suffer greatly from this. The median, however, as a measure of the "value in the middle", will not be so affected and a better estimation is expected.

We should always report both results, and conclude that whenever the final function evaluation is approximately the same with both the mean and the median, the solution is statistically acceptable for that particular problem.

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