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.
>>>>
>>>
>>
>
