julia training slides for 2022-06-08

opening early, there's still a tiny bit of TODOs

Guess this is valid as a 1st draft now

2022/2022-06-08_JuliaForNewcomers/slides/distributed.md

@@ -25,10 +25,17 @@
 
 # Basic parallel processing
 
-**Using Threads:**
+**Using `Threads`:**
 
 1. start Julia with parameter `-t N`
-2. parallelize any loops with `Threads.@threads`
+2. parallelize (some) loops with `Threads.@threads`
+
+```julia
+a = zeros(100000)
+Threads.@threads for i = eachindex(a)
+    a[i] = hardfunction(i)
+end
+```
 
 **Using `Distributed`:**
 
@@ -38,6 +45,8 @@ addprocs(N)
 newVector = pmap(function, oldVector)
 ```
 
+We will use the `Distributed` approach.
+
 
 
 # How to design for parallelization?
@@ -53,7 +62,7 @@ newVector = pmap(function, oldVector)
   - parallelize programs using `pmap` and `dmapreduce` (DistributedData.jl)
 - Decompose more advanced programs into *tasks with dependencies*
   - Dagger.jl
-  - `make -jN` is a surprisingly good tool for parallelization!
+  - `make -jN` may be a surprisingly good tool for parallelization!