Simon SimonDanisch

Always run wsl --update first. Also make sure to run Windows update + make sure all GPU drivers are up to date (google $(GPU_NAME) update treiber windows).

OpenSuse:

Install OpenSuse tumbleweed via appstore. Then:

sudo zypper refresh
sudo zypper update

julia --project=. -e "using Pkg; pkg"add RadeonProRender#sd/moah GeometryBasics RadeonProRender_jll@2.2.7"

executed with:

julia --project=. --check-bounds=(yes|no) rpr_mwe.jl

Diff of the llvm code: https://www.diffchecker.com/chE5Jtim

	blueprint:
	name: Flur Licht Occupancy mit Helligkeit
	description: Licht an wenn Bewegung erkannt und es dunkel ist, aus wenn keine Bewegung
	domain: automation
	input:
	occupancy_sensor:
	name: Occupancy Sensor
	selector:
	entity:
	domain: binary_sensor

	blueprint:
	name: Philips Hue Dimmer RWL022 (Z2M)
	description: Toggle + Dimmen mit Hue Dimmer RWL022 via Zigbee2MQTT
	domain: automation
	input:
	remote_name:
	name: Zigbee2MQTT Gerätename
	description: Exakter Name des Dimmers in Zigbee2MQTT
	selector:
	text:

	# GPU-accelerated SDF brick evaluation via type-specialized operations
	#
	# The SDF.Node tree is converted into nested structs where each operation is
	# a concrete type parameter. Julia specializes `eval_sdf` on the full tree
	# type, and KernelAbstractions compiles it to GPU code. No command buffer,
	# no interpreter - just compiled arithmetic.
	#
	# Example:
	# gpu_tree = to_gpu_op(sdf_node)
	# # typeof(gpu_tree) == GPUUnion{GPUTranslation{GPUSphere}, GPURect}

	using WGLMakie, Bonito

	MSGS = Dict()
	function run_example!(figfunc, name, testfunc)
	f, ax, pl = figfunc()
	display(f)
	_testfunc() = begin
	testfunc(f, ax, pl)
	if isdefined(WGLMakie, :poll_all_plots)
	WGLMakie.poll_all_plots(f.scene)

	ARG IMAGE=nvidia/cuda:12.1.1-devel-ubuntu22.04
	FROM $IMAGE

	ARG JULIA_RELEASE=1.9
	ARG JULIA_VERSION=1.9.4

	# julia

	RUN apt-get update && \
	DEBIAN_FRONTEND=noninteractive \

	using Preferences
	for uuid in ["ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a", "e9467ef8-e4e7-5192-8a1a-b1aee30e663a",
	"276b4fcb-3e11-5398-bf8b-a0c2d153d008", "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"]
	set_preferences!(Base.UUID(uuid), "precompile_workload" => false; force=true)
	end

	# Super simple SIMD implementation for Complex Numbers
	struct ComplexSIMD{N,T}
	re::NTuple{N,T}
	im::NTuple{N,T}
	end
	Base.abs2(z::ComplexSIMD) = z.re .* z.re .+ z.im .* z.im
	Base.:()(a::ComplexSIMD, b::ComplexSIMD) = ComplexSIMD(a.re . b.re .- a.im .* b.im, a.re .* b.im .+ a.im .* b.re)
	Base.:(+)(a::ComplexSIMD, b::ComplexSIMD) = ComplexSIMD(a.re .+ b.re, a.im .+ b.im)

	function mandelbrot_kernel(c::ComplexSIMD{N,T}) where {N,T}

	using Mmap, TextParse
	using TextParse: Record, Field, Numeric, tryparsenext, isnull

	struct MMapString{T} <: AbstractVector{Char}
	x::T
	end
	MMapString(path::String) = MMapString(Mmap.mmap(open(path), Vector{UInt8}))
	# we only need to overload the iteration protocol for TextParse to work!
	Base.Base.@propagate_inbounds Base.getindex(x::MMapString, i) = Char(x.x[i])
	Base.Base.@propagate_inbounds function Base.iterate(x::MMapString, state...)