carstenbauer

function pi_animation()
    N = 1000
    Random.seed!(123)

    # setup figure
    f = Figure(resolution = (1000,400))
    dart_fig = f[1,1]
    dart_ax = Axis(dart_fig, aspect=1, title="Monte-Carlo-Simulation")
    approx_fig = f[1,2]
    approx_ax = Axis(approx_fig, title="Quality of Approximation")

carstenbauer

function run_experiment(N; saverate=1)
    hits = 0
    dist = Uniform(-1,1)
    is = Int64[]
    pis = Float64[]
    
    for i in 1:N
        x, y = rand(dist), rand(dist)
        # speed-up computation by omiting sqrt
        if (x^2 + y^2) <= 1

carstenbauer

# Equations:
# ∂P/∂t = -k(∂Vx/∂x + ∂Vy/∂y)
# ∂Vx/∂t = -1/ρ ∂P/∂x
# ∂Vy/∂t = -1/ρ ∂P/∂y

# => Update rules:
# Vx = Vx - Δt/ρ ΔP/Δx
# Vy = Vy - Δt/ρ ΔP/Δy
# P = P - Δt*k (Δ(Vx)/Δx + Δ(Vy)/Δy)

carstenbauer

using Test: @test
using Base.Threads: nthreads
using BenchmarkTools: @btime
using OhMyThreads: @tasks, tmap!, tmapreduce, chunks
using ThreadPinning
pinthreads(:cores)

# naive, math implementation
function matvec_row!(y, A, x)
    fill!(y, zero(length(y)))