I don't get it, but it doesn't like something about concatenating the
number and the array. If you convert the 1 to an array first, it works. I
thought it was because 1 is an integer and you're joining it with an array
of float 0's, but using 1. doesn't work either.
function version1(N)
b = [[1]; zeros(N-1)]
println(typeof(b))
for k = 1:N
for j = 1:N
b[j] += k
end
end
end
This has no type issues.
On Thursday, April 21, 2016 at 2:08:52 PM UTC-4, Jeremy Kozdon wrote:
>
> Thanks. I figured it was something along these lines. I'd forgotten about
> the "@code_warntype" macro
>
> On Thursday, April 21, 2016 at 10:45:17 AM UTC-7, Daniel O'Malley wrote:
>>
>> It looks like the type inference is failing in version1. If you do
>> "@code_warntype version1(1000)", it shows that it is inferring the type of
>> b as Any.
>>
>> On Thursday, April 21, 2016 at 11:32:27 AM UTC-6, Jeremy Kozdon wrote:
>>>
>>> In a class I'm teaching the students are using Julia and I couldn't for
>>> the life of me figure out why one of my students codes was allocating a lot
>>> of memory.
>>>
>>> I finally paired it down the following example that I don't understand:
>>>
>>> function version1(N)
>>> b = [1;zeros(N-1)]
>>> println(typeof(b))
>>> for k = 1:N
>>> for j = 1:N
>>> b[j] += k
>>> end
>>> end
>>> end
>>>
>>>
>>> function version2(N)
>>> b = zeros(N)
>>> b[1] = 1
>>> println(typeof(b))
>>> for k = 1:N
>>> for j = 1:N
>>> b[j] += k
>>> end
>>> end
>>> end
>>>
>>> N = 1000
>>> println("compiling..")
>>> @time version1(N)
>>> version2(N)
>>> println()
>>> println()
>>>
>>> println("Version 1")
>>> @time version1(N)
>>> println()
>>>
>>> println("Version 2")
>>> @time version2(N)
>>>
>>> The output of this (without the compiling output) in v0.4.5 is:
>>>
>>> Version 1
>>> Array{Float64,1}
>>> 0.092473 seconds (3.47 M allocations: 52.920 MB, 3.24% gc time)
>>>
>>> Version 2
>>> Array{Float64,1}
>>> 0.001195 seconds (27 allocations: 8.828 KB)
>>>
>>> Both version produce the same type for Array b, but in version1 every
>>> time through the loop allocation happens and in the version2 the only
>>> allocation is of the initial array.
>>>
>>> I've not run into this one before (because I would never do version1),
>>> but as all of us that teach know students will always surprise you with
>>> their approaches.
>>>
>>> Any help understanding what's going on would be appreciated.
>>>
>>
