Simple code run @time 1.7, 1.8, 1.9 difference

New to Julia
1 
function sum_n(n)
	s = 0
	for i in 1:n
		s += i
	end
	return s
end

@time sum_n(1_000_000_000)

On Windows 11 x64

1.7.3 : 0.000001 seconds
1.8.5 : 2.033023 seconds
1.9 rc2 : 5.878953 seconds

Did I do something wrong?

P.S> Sample code from ‘Julia for Data Analysis’ chapter 1.

2

Seeing the same timing, though running on 1.9 without the @time macro it is instant (or at least not a few seconds).

Introducing f() = sum_n(1_000_000_000) and running @code_llvm f() we see that the compiler can simplify the call to a constant in all versions.

So it seems there is some change in the @time macro that makes the later versions not allow that optimization when timing, while 1.7 does.

1 Like
3

Perhaps to do with compiling the first time for the first run? Though from what I understand this TTFX is dealt with in 1.9x. Have you tried using BenchmarkTools? See here: Home · BenchmarkTools.jl

4

running on 1.9 without the @time macro it is instant (or at least not a few seconds).

confirmed.
It is also same with 1.8.5, with no ‘@time’ macro, it is instant, too.
I don’t understand @code_llvm f() things, but even to me, it looks like @time macro in later version does not allow optimized behavior.

Thank you for detailed explanation.

5

It is same first run with 1.7, but different run time.

6

There have been changes to @time since 1.7 to prevent the compiler from beating benachmarks in certain situations, so in 1.7 the loop isn’t actually executed but replaced with the return value by LLVM.

1 Like
7

I don’t think this is correct. What’s actually happening is that @time appears to inhibit normal optimisation on 1.9. You can see that on all versions of Julia, the compiler figures out the analytical solution to the problem. Yet somehow, @time makes it slow in Julia 1.9.

It appears to be solved on recent master though.

Edit: I’ve made an issue: `@time` inhibits optimisation on 1.9 · Issue #49554 · JuliaLang/julia · GitHub

Edit2: This is a known regression, and a fix is being backported: Backport to 1.9: Improve performance of global code by emitting fewer atomic barriers. by maleadt · Pull Request #49411 · JuliaLang/julia · GitHub

6 Likes
8

You are right. It is a duplicate of [Spurious performance regression in Julia 1.8 vs 1.7 for `@time` in top-level-scope · Issue #47561 · JuliaLang/julia · GitHub].

Actually, #47561 was opened almost 5 months ago with the same sample code by the writer of the book “Julia for Data Analysis”, from which I got sample code in this question.

Pleased to know that fix is already merged.

Thank for letting me know the issue.