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

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

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

julia> function vector_dot(B, C)
           a = zero(eltype(B))
           for i in eachindex(B,C)
               @inbounds a += B[i] * C[i]
           end
           return a
       end
vector_dot (generic function with 1 method)

julia> function vector_dot_macro(B, C)

carstenbauer

using DelimitedFiles
using LinearAlgebra
using Test

function readqcd(fname::AbstractString; verify=true)
    open(fname, "r") do f
        header_str = readuntil(f, "END_HEADER\n")
        header_mat = readdlm(IOBuffer(header_str), '=')
        header = Dict(strip(header_mat[i, 1]) => header_mat[i, 2] for i in 2:size(header_mat, 1))

carstenbauer

using CUDA
using BenchmarkTools
using Statistics
using UnicodePlots

# bandwidth test
abstract type BytesSIPrefixed end
struct B <: BytesSIPrefixed
    value::Int64
end

carstenbauer

# This file is machine-generated - editing it directly is not advised

julia_version = "1.7.1"
manifest_format = "2.0"

[[deps.ArgTools]]
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"

[[deps.Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"

carstenbauer

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "202a91a0-8ac9-4d47-a628-85786980cbb2",
   "metadata": {},
   "source": [
    "# CPU\n",
    "\n",
    "## Single-threaded sparse mat-vec product"

carstenbauer

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "202a91a0-8ac9-4d47-a628-85786980cbb2",
   "metadata": {},
   "source": [
    "# CPU\n",
    "\n",
    "## Single-threaded sparse mat-vec product"

carstenbauer

print("Loading modules...")
using BenchmarkTools
using CUDA
using DataFrames
using CSV
using Printf
println("done!")

const a = 3.1415f0