Comments URL: https://news.ycombinator.com/item?id=47432270

Points: 1

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=45938295

Points: 4

# Comments: 0

However, I did hear a few times about cases where you'd trade better average performance that comes with spikes (e.g. in terms of response time) in favor of worse average performance that has lower variance.

Comments URL: https://news.ycombinator.com/item?id=43326043

Points: 9

# Comments: 2

I wrote the code that you suggested (LMK if I understood your points): https://godbolt.org/z/jW4o3cnh3

And here's the benchmark output, on my machine: https://0x0.st/8SsG.txt (v1 is std::count_if(), v2 is the optimization from my blog post, and v3 is what you suggested).

v2 is faster, but v3 is still quite fast.

I modified the footnote to get rid of the misleading statements regarding the 'backfiring' of the optimization. :)

So the case that you described has 2 layers. The internal std::count_if() layer, which has a 64-bit counter, and the 'return' layer of the count_even_values_v1() function, which has an 8-bit type. In this case, Clang propagates the 8-bit type from the 'return' layer all the way to the inner std::count_if() layer, which effectively means that you're requesting an 8-bit counter, and thus Clang generates the efficient vectorization.

However, say that you have the following 3 layers: (1) internal std::count_if() layer with a 64-bit counter; (2) local 8-bit variable layer, to which the std::count_if() result gets assigned; (3) 'return' layer with a 64-bit type. In this case the 64-bit type from layer 3 gets propagated to the inner std::count_if() layer, which will lead to a poor vectorization. Demo: https://godbolt.org/z/Eo13WKrK4 . So this downwards type-propagation from the outmost layer into the innermost layer doesn't guarantee optimality. In this case, the optimal propagation would've been from layer 2 down to layer 1 and up to layer 3.

Note: I'm not familiar with how the LLVM optimization pass does this exactly, so take this with a huge grain of salt. Perhaps it does indeed 'propagate' the outmost type to the innermost layer. Or perhaps the mere fact that there are more than 2 layers makes the optimization pass not happen at all. Either way, the end result is that the vectorization is poor.

I've looked at the generated assembly for such a solution and it doesn't look great. I'm expecting a significant speed penalty, but I haven't had the time to test it today. Will probably do so tomorrow.

"It's also debatable whether or not Clang's 'optimization' results in better codegen in most cases that you care about. The same optimization pass can backfire pretty easily, because it can go the other way around too. For example, if you assigned the `std::count_if()` result to a local `uint8_t` value, but then returned that value as a `uint64_t` from the function, then Clang will assume that you wanted a `uint64_t` accumulator all along, and thus generates the poor vectorization, not the efficient one."

Comments URL: https://news.ycombinator.com/item?id=43302394

Points: 134

# Comments: 45

Yes.

> If you run LLVM 18's `opt` on bytecode generated by Clang 19 and then compile it, does it also generate the same bad assembly?

No. If you pass the LLVM IR bitcode generated by Clang 18 to Clang 19, then the assembly is good.

I called it a 'Clang regression' in the sense that the way in which I discovered and tested this difference in performance was via Clang. So from a typical user's perspective (who doesn't care about the inner workings and distinct components of Clang), this is a 'Clang regression'.

This whole investigation started because I was writing some Rust code with a couple of small `match`es, and for some reason they weren't being optimized to a lookup table. I wrote a more minimal reproduction of that issue in C++ and eventually found the Clang regression. Since Rust also uses LLVM, `match`es suffer from the same regression (depending on which Rust version you're using).

Comments URL: https://news.ycombinator.com/item?id=43148694

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=43107244

Points: 21

# Comments: 3