If I had to guess, your problem is type stability. Are you using NamedTuples to store your state and items you’re iterating over? If the keys and are not all the same and value types don’t stay the same (e.g. something initialized as zero(Int) and then accumulated into with Float64s) then performance will suffer. Another possibility is that you have a data type not is not concrete in an inner loop. For example, Array{Real} will be slower than Array{Float64} because an array of Reals has to support arrays mixing Float32 and Float64. If you had this in a function definition the likely correct thing to do is Array{<:Real}, which means the element type of the array must be a subtype of Real. Maybe even better, just drop the type annotations. They very, very rarely improve performance because Julia relies on compile time type inference.
Failed or bad type inference is almost always the cause of performance issues in Julia. Getting a feel for when the compiler can infer things or not takes practice, but it’s a lot easier than the semantics of generic programming systems IMO.
The REPL is really great for learning. If you type “Array{Int} == Array{Int, 1}” the result is false. If you type “?Array” it prints the docstring which gives some guidance on how to use one versus the other.
Failed or bad type inference is almost always the cause of performance issues in Julia. Getting a feel for when the compiler can infer things or not takes practice, but it’s a lot easier than the semantics of generic programming systems IMO.
The REPL is really great for learning. If you type “Array{Int} == Array{Int, 1}” the result is false. If you type “?Array” it prints the docstring which gives some guidance on how to use one versus the other.