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.
>>
>
