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.
>>>
>>
>
