Thanks Tom. Pweh, that's what I suspected. I glanced at boot.jl, and it doesn't seem Julia has a typealias for doubles. I'll define my own to check for 32 vs. 64-bit systems.
On Monday, 25 August 2014 15:10:30 UTC-4, Tomas Lycken wrote: > > Actually, Int (and UInt) are aliases to the “native size integer”, so if > you specify Int you will get Int32 on a 32-bit system and Int64 on a > 64-bit system. So no, don’t change my_var::Int to my_var::Int32 - that’ll > make your code *worse* on 64-bit systems ;) > > // T > > On Monday, August 25, 2014 9:05:06 PM UTC+2, Roy Wang wrote: > > >> Thanks guys. So to clarify: FloatingPoint is not a concrete types, so >> explicitly defining variables or function inputs using it will not speed >> things up. Instead, I should use Float64, Float32, etc. >> >> Is Int an abstract type as well? I'm wondering if I should go back and >> rename everything my_var::Int to my_var::Int32. >> >> John: I couldn't find the mutate!() function in the Julia Standard >> Library v0.3. Do you mean my own function that mutates the source array? >> >> On Monday, 25 August 2014 14:54:14 UTC-4, Patrick O'Leary wrote: >>> >>> On Monday, August 25, 2014 12:28:00 PM UTC-5, John Myles White wrote: >>>> >>>> Array{FloatingPoint} isn't related to Array{Float64}. Julia's type >>>> system always employs invariance for parametric types: >>>> https://en.wikipedia.org/wiki/Covariance_and_contravariance_(computer_science) >>>> >>>> <https://www.google.com/url?q=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FCovariance_and_contravariance_%28computer_science%29&sa=D&sntz=1&usg=AFQjCNH5Mpuwh71o9dv0_TDx9OcMvvKfWg> >>>> >>> >>> To underline this point a bit, it's even a bit worse than that: >>> Array{FloatingPoint} will work just fine for a lot of things, but it stores >>> all elements as heap pointers, so array-like operations (such as linear >>> algebra routines) will often be extremely slow. >>> >>> As a rule, you almost never use an abstract type as the type parameter >>> of a parametric type for this reason. Where you wish to be generic over a >>> specific family of types under an abstract type, you can use type >>> constraints: >>> >>> function foo{T<:FloatingPoint}(src::Array{T,1}) >>> ... >>> end >>> >>> But often type annotations can be omitted completely. >>> >> >