summerschool_simtech_2023/material/5_fri/optimizing_julia/optimizing_julia.md

# Optimizing Julia code

This session provides an introduction to optimizing Julia code.
The examples are developed with Julia v1.9.3.

First, we install all required packages

````julia
import Pkg
Pkg.activate(@__DIR__)
Pkg.instantiate()
````

The markdown file is created from the source code using
[Literate.jl](https://github.com/fredrikekre/Literate.jl).
You can create the markdown file via

````julia
using Literate
Literate.markdown(joinpath(@__DIR__, "optimizing_julia.jl");
                  flavor = Literate.CommonMarkFlavor())
````

## Basics

The Julia manual contains basic information about performance.
In particular, you should be familiar with

- https://docs.julialang.org/en/v1/manual/performance-tips/
- https://docs.julialang.org/en/v1/manual/style-guide/

if you care about performance.

## Example: A linear advection simulation

This is a classical partial differential equation (PDE) simulation setup.
If you are not familiar with it, just ignore what's going on - but we need
an example. This one is similar to several problematic cases I have seen
in practice.

First, we generate initial data and store it in a file.

````julia
using HDF5
x = range(-1.0, 1.0, length = 1000)
dx = step(x)
h5open("initial_data.h5", "w") do io
    u0 = sinpi.(x)
    write_dataset(io, "x", collect(x))
    write_dataset(io, "u0", u0)
end
````

Next, we write our simulation code as a function - as we have learned from
the performance tips!

````julia
function simulate()
    x, u0 = h5open("initial_data.h5", "r") do io
        read_dataset(io, "x"), read_dataset(io, "u0")
    end

    t = 0
    u = u0
    while t < t_end
        u = u + dt * rhs(u)
        t = t + dt
    end

    return t, x, u, u0
end

function rhs(u)
    du = similar(u)
    for i in eachindex(u)
        im1 = i == firstindex(u) ? lastindex(u) : (i - 1)
        du[i] = -(u[i] - u[im1]) / dx
    end
    return du
end
````

Now, we can define our parameters, run a simulation,
and plot the results.

````julia
using Plots, LaTeXStrings

t_end = 2.5
dt = 0.9 * dx
t, x, u, u0 = simulate()
plot(x, u0; label = L"u_0", xguide = L"x")
plot!(x, u; label = L"u")
````

Maybe you can already spot problems in the code above.
Anyway, before you begin optimizing some code, you should
test it and make sure it's correct. Let's assume this is the
case here. You should write tests making sure that the code
continues to work as expected while we optimize it. We will
not do this here right now.

## Profiling

First, we need to measure the performance of our code to
see whether it's already reasonable. For this, you can use
`@time` - or better BenchmarkTools.jl.

````julia
using BenchmarkTools
@benchmark simulate()
````

This shows quite a lot of allocations - typically a bad sign
if you are working with numerical simulations.

There are also profiling tools available in Julia.
Some of them are already loaded in the Visual Studio Code
extension for Julia. If you prefer working in the REPL,
you can install ProfileView.jl (`@profview simulate()`) and
PProf.jl (`pprof()` after creating a profile via `@profview`).

````julia
@profview simulate()
````

Task: Optimize the code!

## Introduction to generic Julia code and AD

One of the strengths of Julia is that you can use quite a few
modern tools like AD. However, you need to learn Julia a bit
to use everything efficiently.
Here, we concentrate on ForwardDiff.jl.

````julia
using ForwardDiff
using Statistics

function foo0(x)
    vector = zeros(typeof(x), 100)
    foo0!(vector, x)
end

function foo0!(vector, scalar)
    for i in eachindex(vector)
        vector[i] = atan(i * scalar) / π
    end

    for _ in 1:1000
        for i in eachindex(vector)
            vector[i] = cos(vector[i])
        end
    end

    return mean(vector)
end

let x = 2 * π
    @show foo0(x)
    @show ForwardDiff.derivative(foo0, x)
    @benchmark foo0($x)
end
````

The code above is basically a fixed point iteration.
By doing some analysis, one could figure out that it
should converge to the solution `x` of `x == cos(x)`,
the "Dottie number". See https://en.wikipedia.org/wiki/Dottie_number

````julia
using SimpleNonlinearSolve

function dottie_number()
    f(u, p) = cos(u) - u
    bounds = (0.0, 2.0)
    prob = IntervalNonlinearProblem(f, bounds)
    sol = solve(prob, ITP())
    return sol.u
end

dottie_number()
````

Next, we introduce a custom struct. Can you spot the problems?

````julia
struct MyData1
    scalar
    vector
end

function foo1(x)
    data = MyData1(x, zeros(typeof(x), 100))
    foo1!(data)
end

function foo1!(data)
    (; scalar, vector) = data

    for i in eachindex(vector)
        vector[i] = atan(i * scalar) / π
    end

    for _ in 1:1000
        for i in eachindex(vector)
            vector[i] = cos(vector[i])
        end
    end

    return mean(vector)
end

let x = 2 * π
    @show foo1(x)
    @show ForwardDiff.derivative(foo1, x)
    @benchmark foo1($x)
end
````

We can fix type instabilities by introducing types explicitly.
But we lose the possibility to use AD this way!

````julia
struct MyData2
    scalar::Float64
    vector::Vector{Float64}
end

function foo2(x)
    data = MyData2(x, zeros(typeof(x), 100))
    foo1!(data)
end

let x = 2 * π
    @show foo2(x)
    @benchmark foo2($x)
end

let x = 2 * π
    ForwardDiff.derivative(foo2, x)
end
````

Thus, we need parametric types!

````julia
struct MyData3{T}
    scalar::T
    vector::Vector{T}
end

function foo3(x)
    data = MyData3(x, zeros(typeof(x), 100))
    foo1!(data)
end

let x = 2 * π
    @show foo3(x)
    @show ForwardDiff.derivative(foo3, x)
    @benchmark foo3($x)
end
````

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