Update examples

roflmaostc · Feb 10, 2024 · 1c5e4e3 · 1c5e4e3
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diff --git a/examples/0_example_radon_backproject.jl b/examples/0_example_radon_backproject.jl
@@ -29,23 +29,35 @@ using RadonKA
 using IndexFunArrays, ImageShow, Plots, ImageIO, PlutoUI, PlutoTest, TestImages
 
 # ╔═╡ 03bccb92-b47f-477a-9bdb-74cc404da690
-using KernelAbstractions, CUDA, CUDA.CUDAKernels
+using CUDA
 
 # ╔═╡ 6f6c8d28-5a54-440e-9b7a-52e1fce959ab
-md"# Activate example environment"
-
-# ╔═╡ f5e2024b-deaf-4344-b610-a4b956abbfaa
-md"# Load CUDA
-In case you have no CUDA card available, it will run on CPU :)
+md"# Load packages
+On the first run, Julia is going to install some packages automatically. So start this notebook and give it some minutes to install all packages. 
+No worries, any future runs will be much faster to start!
 "
 
 # ╔═╡ ef92457a-87c0-43bf-a046-9fe82afbbe13
 begin
 	use_CUDA = Ref(true && CUDA.functional())
 	var"@mytime" = use_CUDA[] ? CUDA.var"@time" : var"@time"
+	"""
+		togoc(x)
+
+	Stands for "to GPU or CPU". In case you have a GPU, it moves the array to the GPU.
+	In case CUDA is runable, it will just leave it on the CPU
+	"""
 	togoc(x) = use_CUDA[] ? CuArray(x) : x
 end
 
+# ╔═╡ f5e2024b-deaf-4344-b610-a4b956abbfaa
+md""" ## CUDA
+Thanks to Julia multiple dispatch and [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) our code can run on CUDA GPUs.
+Big reconstructions can run 5-20 times faster on a CUDA GPU!
+
+Your CUDA is functional: **$(use_CUDA[])**
+"""
+
 # ╔═╡ 810aebe4-2c6e-4ba6-916b-9e4306df33c9
 TableOfContents()
 
@@ -135,25 +147,28 @@ simshow(Array(filtered_bproj[:, :, i_z4]))
 # ╔═╡ a29be556-174a-4ec5-962d-9fdf203d51aa
 backproject ≈ Array(backproject_cu)
 
+# ╔═╡ fd2bab7a-7ef6-42aa-bcb5-2a52bffc61db
+
+
 # ╔═╡ Cell order:
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-# ╠═783f05e0-2640-4ecd-8c19-1c15a99ee294
+# ╟─783f05e0-2640-4ecd-8c19-1c15a99ee294
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@@ -169,3 +184,4 @@ backproject ≈ Array(backproject_cu)
 # ╟─bc6e2d40-fcd1-4d7b-8f96-3d4d9e4336de
 # ╠═eee184e3-8d5d-42fb-81fb-a5d7e59a083a
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+# ╠═fd2bab7a-7ef6-42aa-bcb5-2a52bffc61db