As they observe, Ninja gets to be fast mostly by cheating: it avoids a lot of work by saying many things are just out of scope for Ninja to do, and that means it is a useful a target to race against. (Funny thing: when I wrote Ninja I was misremembering how fast an earlier build system was so I kept trying to make it faster. So don't treat it as a lower bound, I just made it up!)
I comment here to say I find the explanation for 'why' in this post unsatisfying. They mention three design decisions.
The first one is a criticism of CMake, not Ninja (?), so I don't think it can be why. I might have misunderstood?
The second reason given is doing some work like header dependencies in multiple threads. This is the most plausible reason to me but it still feels unlikely. It's a very small amount of work: the post mentions 300 compiles, so maybe parsing 300 small text files?
The third is that they run the compiler up front an additional time to gather headers, which is strictly more work than Ninja. There is some hand waving about file access patterns but I am skeptical; if the end-to-end build time is 3 seconds then the project is small enough to all fit in kernel caches. They also mention doing other things like invoking the compiler to get version information. This seems like it would dwarf any performance gain from number 2.
Maybe it's just my own curiosity, I think this post would be better if it had a better explanation for the reason. I'm not disputing the result, I just think the result should make you suspicious that something else is going on, and you might learn something from that! You could for example explore whether it's the header dependency thing by profiling the Ninja invocation and seeing if it's waiting for CPU or waiting for tasks to execute.
(If I had to guess without looking at any of the involved code, I would predict it's something about how CMake generates the build, like it introduces serialization in a place where build2 is parallel, or it adds some extra build steps like gathering the current git hash into a header file or something.)
Since they had to rewrite the build file for their program I also assume that something is missing. Didn’t see any mention of verifying that.
I also really didn’t like their denigrating tone. It totally turns me off trying build2, because it seems they don’t understand the point of separating build stages like environment setup (getting dependencies), configure, native build, cross build, packaging. I am a very happy ninja user instead of a batteries-included solution because it does its one job well and can be used very flexibly. I personally detest cmake, so I use nix + own configure script + ninja.
The blog is also wrong about ninja being unable to call configure, but I intentionally don’t want that (I want the build stages to communicate in one direction for sanity).
That's easy to do if you control the whole pipeline and can integrate all the features together, not so much with the CMake model unfortunately. I think it would be nice if CMake had Ninja integrated as a library, it could lead to some nice optimizations later.
And if they are in their own targets, they are not really an issue (they would serialize everything that depends on them as you'd expect), but if you have them in a library grouped with other files to compile, then the whole library compilation is serialized.
And obviously worse if you also have to build the generator for the generated files, but that's not a big surprise, you can observe that in full builds of Chromium or its libraries too waiting for protoc if you crank the parallelization a lot.
When, from first principles, something shouldn't be faster, yet it is, you dig and dig until you understand. You don't just say your shit rules and the other thing sux0rz. If you do, you'll regret it: if you don't understand how you got a win, you don't understand how to keep it.
Something is wrong here. Which compression algorithm is being used here and how much has it been tuned? A core hypothesis of the likes of zram is that disk access is so slow (even NVME), that you can often beat it with the bit-rate of decompression.
1. Is something slow like gzip being used?
2. Is the compression effort over-tuned for size? Do some space benchmarks and make sure that you aren't saving a few dozen MB on GBs of data.
zstd, with 1-3 effort (you may even find negative is a overall win), and a trained dictionary (your data does all look identical) is probably a good start.
> The first one is a criticism of CMake, not Ninja (?), so I don't think it can be why.
Fair enough. The point I was making is that if you want to compete with Ninja, you cannot leave any potential performance gains on the table.
> The second reason given is doing some work like header dependencies in multiple threads. This is the most plausible reason to me but it still feels unlikely.
We are talking about ~2% performance difference here. Parallelizing even a small amount of work across 24 threads rather that doing it serially saving a percent or two feels plausible to me.
> There is some hand waving about file access patterns but I am skeptical; if the end-to-end build time is 3 seconds then the project is small enough to all fit in kernel caches.
It fits into the system's file cache unless there is memory pressure, like one would expect from having 24 C++ compiler jobs running in parallel. We actually measured this in isolation (with more detailed results in the linked article) and it has a noticeable effect.
> They also mention doing other things like invoking the compiler to get version information. This seems like it would dwarf any performance gain from number 2.
I measured this, it costs 70ms or ~2% of the overall time.
ladams•1h ago
This claim isn't supported by the author's measurements?
karen_arutyunov•1h ago
no?
boris•1h ago