There is a function in Bogumił Kamiński’s book Julia for Data Analysis(page 7) with the purpose to show off the optimizations done by Julia compiler.
function sum_n(n)
s = 0
for i in 1:n
s += i
end
return s
end
In the book, the code shown superb results in runtime, like 0.000001 seconds, where running the Julia v1.7. However, I tested the same function in both v1.8.3, v1.8.5 and v1.9.0, the results were not the case, which yielded about one second. Thus, I am wondering that what happened from v1.7 to v1.8 and above on the loops?
A small remark. First of all and as a rule of thumb, you should benchmark with @time on the second call of the function. That is because the first time you call the function it will also compile it and essentially @time will also include compilation time, which is something you typically are not interested in.
Also using BenchmarkTools.jl as mentioned above will help a deal.
This isn’t compilation time. Running @time repeatedly shows it taking 1.7 seconds, but running @btime on it takes nanoseconds (and this is easy to see since @btime runs faster than @time which shouldn’t be possible).
While there were almost equivalent performance in terms of running @btime, but still the difference about a nanosecond. In the book, Bogumił explained that the consecutive sum would be optimized as to n(n+1)/2. I am not sure it is still the case in v1.8.0 or above. How can I check it, given that I don’t know assembly language.
Hm. Thanks for posting this back! This is helpful.
I cannot explain why it is apparently slower by a single nanosecond – perhaps a regression somewhere on 1.9 that hasn’t been caught yet? – but I’d say the speeds are still comparable as you mentioned. I am sure a speed hacker like @Oscar_Smith or @Elrod could probably comment more. Just looking at this though from my perspective, this does seem to be a minor regression on 1.9.
P.S. I updated the title of your post to make it a bit more precise for other Julians to see/understand.
