Excellent overview of the ThreadsX.jl package. A small point, but from about 35:26 onwards we repeatedly see an expression like "!digitsin(digits, i) ? 1.0/i : 0". While this works it is better in Julia to make sure expressions like this (or any function) returns a consistent type. The first (if true) expression will be Float64, whereas the second (if false) is Int64 and you suffer about a 10% performance hit to do type conversions later.
Excellent overview of the ThreadsX.jl package. A small point, but from about 35:26 onwards we repeatedly see an expression like "!digitsin(digits, i) ? 1.0/i : 0". While this works it is better in Julia to make sure expressions like this (or any function) returns a consistent type. The first (if true) expression will be Float64, whereas the second (if false) is Int64 and you suffer about a 10% performance hit to do type conversions later.
best example of simple parallel Julia to-date!
can i get this ppt slide?