[Sesse__]

Yes, since the inner nodes duplicate information, it uses more space (roughly twice as much). I've found them generally most effective for things like merging 256 streams or doing Dijkstra with not super-many nodes (e.g. Contraction Hierarchies). If you have millions or billions of entries, then cache considerations start becoming more important than branch mispredictions and you want something like a B-heap.

[za_creature]

That's a nice niche you found (spoken from one heap fan to another) but I have to say I strongly disagree with your use of *roughly* twice as much

At best you were off by one but in the context of performance, you'd want to assign that extra to a super-root of ±inf in order to save log2n extra range checks per heap-up, no?

[Sesse__]

I don't think I understand what you are saying. Anyway, if you are strapped for space, do not use a winner tree.

[za_creature]

Ah, I meant that for a classic heap, it's convenient to assign h[0] to the limit of your goal function (e.g. -inf for a min heap) cause that way you can skip the while i>0 check and just do while h[i>>1] > min(h[i], h[i+1]), asserting that the condition is definitely false when i < 2

I guess that it's not as important when you're explicitly willing to pay the piper and run the full log2n iterations just for the sake of branch predictability.

Thanks for the algo though, before today I never would've thought about doing a branchless downheap with i = i<<1 + h[i<<1] != h[i]