Sorry I was not clear enough. They are not the same but there is no reason to use Float32 on a 32 bit system and Float64 on a 64 bit system. On both 32bit and 64bit CPUs you will usually have 80bit floating point units. So both will be equally fast (if we ignore caching for a moment). In contrast integers and in particular array indices will be slower if one uses Int64 on a 32bit system. Therefore we have Int as a shortcut for the "native" integer type.
Cheers, Tobi Am Montag, 25. August 2014 21:57:40 UTC+2 schrieb Roy Wang: > > I didn't know Float64 and Float32 are the same on 32-bit systems. Thanks. > > On Monday, 25 August 2014 15:48:30 UTC-4, Tobias Knopp wrote: >> >> Thats for a reason. Float64 and Float32 are the same on 64 and 32 bit >> computers. Its only the integer types where this matters. >> >> Am Montag, 25. August 2014 21:38:44 UTC+2 schrieb Roy Wang: >>> >>> 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. >>>>>> >>>>> >>>> >>>