Every once in a while something breaks, usually around exotic use of templates. But on the whole we love it, and we'd have to do so much ongoing refactoring to keep things workable without them.
Update: I now recall those numbers are from a partial experiment, and the full deployment was even faster, but I can't recall the exact number. Maybe a 2/3 speedup?
And to be clear, it also speeds up the original compilation, but that's not as noticeable because when you're compiling zillions of separate compilation units with massive parallelism, you don't notice how long any given file takes to compile.
People keep saying this and yet I do not know of a good example from a real life project which did this which I can test. This seems very much still an experimental thing.
There are still some features that are missing from compilers, but enough is there that you can target all 3 major compilers and still get most of modules and benefit from them. However if you do this remember you are an early adopter and you need to be prepared to figure out the right way to do things - including fixing things that you get wrong once you figure out what is right.
Also, if you are writing a library you cannot benefit from modules unless you are willing to force all your consumers to adopt modules. This is not reasonable for major libraries used by many so they will be waiting until more projects adopt modules.
Still modules need early adopters and they show great promise. If you write C++ you should spend a little time playing with them in your current project even if you can't commit anything.
https://github.com/pjmlp/RaytracingWeekend-CPP
Also shows how to use static libraries alongside modules.
> C++ 26 reflections have now been voted in. This would get rid of moc entirely, but I really do not see how this will become widely available in the next 5-10 Years+. This would require Qt to move to C++ 26, but only if compiler support is complete for all 3 compilers AND older Linux distros that ship these compilers. For example, MSVC still has no native C++ 23 flag (In CMake does get internally altered to C++ latest aka. C++ 26) , because they told me that they will only enable it is considered 100% stable. So I guess we need to add modules support into moc now, waiting another 10 years is not an option for me .
A while ago I made a small example to test how it would work in an actual project and that uses cmake (https://codeberg.org/JulianGmp/cpp-modules-cmake-example). And while it works™, you can't use any compiler provided modules or header modules. Which means that 1) so you'll need includes for anything from the standard library, no import std 2) you'll also need includes for any third party library you want to use
When I started a new project recently I was considering going with modules, but in the end I chose against it because I dont want to mix modules and includes in one project.
https://github.com/odoo/paper-muncher/blob/main/src/main.cpp
Very cool.
clang++ -std=c++20 Hello.cppm --precompile -o Hello.pcm
clang++ -std=c++20 use.cpp -fmodule-file=Hello=Hello.pcm Hello.pcm -o Hello.out
./Hello.out
I'm used to:
g++ -o hello hello.cpp
I recommend skimming over this issue from Meson:
https://github.com/mesonbuild/meson/issues/5024
Reading the last few blog posts from a developer of Meson, providing some insights why Meson doesn't support modules until now:
That is simple, because C++ inherited C's simplistic, primitive, and unsafe compilation and abstraction model of brute-force textual inclusion. When you scale this to a large project with hundreds of thousands of translation units, every command-line invocation becomes a huge list of flag soup that plain Makefiles become intractable.
Almost all other reasonably-recent programming languages have all of the following:
- a strong coupling of dependency management, building, installation, and publishing tools
- some description of a directed acyclic graph of dependencies, whether it be requirements.txt, cargo.toml, Maven, dotnet and Nuget .csproj files, Go modules, OPAM, PowerShell gallery, and more
- some way to describe the dependencies within the source code itself
C++20 modules are a very good thing, and enforce a stronger coupling between compiler and build tool; it's no longer just some weekend project chucking flags at g++/clang++/cl.exe but analysing source code, realising it needs a, b, c, x, y, z modules, ensuring those modules are built and export the necessary symbols, and then compiling the source at hand correctly. That is what `clang-scan-deps` does: https://clang.llvm.org/docs/StandardCPlusPlusModules.html#di...
I concede there are two problems with C++20 modules: we didn't have a working and correct compiler implementation before the paper was accepted into C++20, and secondly, the built/binary module interface specification is not fixed, so BMIs aren't (yet) portable across compilers.
The Meson developer is notorious for stirring the pot with respect to both the build system competition, and C++20 modules. The Reddit thread on his latest blog post provides a searing criticism for why he is badly mistaken: https://www.reddit.com/r/cpp/comments/1n53mpl/we_need_to_ser...
This doesn’t solve any problem that wasn’t self-inflicted.
I agree on your points about having a working implementation before the paper was accepted, this is why C++ is a mess and will never be cleaned up. I love C++ but man, things like this are plenty.
g++ -c -o hello.o hello.cpp
g++ -o hello hello.o
./hello
When modules were being standardized, there was a discussion on whether there should be any sort of implicit mapping between modules and files. This was rejected, so the build system must supply the information about which module is contained in which file. The result is more flexible, but also more complex and maybe less efficient.
I'm eager to gather info but the weak spots of headers (and macros) are obvious. Probably holding a waiting position for undefined time. At least as long Meson doesn't support them.
Wikipedia contains false info about toolings: https://en.wikipedia.org/wiki/Modules_(C%2B%2B)#Tooling_supp...
Meson doesn't support modules as of 2025-09-11.
PS: I'm into new stuff when it looks stable and the benefits are obvious. But this looks complicated and backing out of complicated stuff is painful, when necessary.
If the tool is intended for complex things I'm not sure I agree. It is nice when hello is simple, but if you can make the complex cases a little easier at the expense of making the simple things nobody does harder I don't know if I care. (note that the example needed the -o parameter to the command line - gcc doesn't have a good default... maybe it should?)
> Online, this number varies widely. The most exaggerated figure I recall is a 26x improvement in project compilation speed after a module-based refactoring.
> Furthermore, if a project uses extensive template metaprogramming and stores constexpr variable values in Modules, the compilation speed can easily increase by thousands of times, though we generally do not discuss such cases.
> Apart from these more extreme claims, most reports on C++20 Modules compilation speed improvements are between 10% and 50%.
I'd like to see references to those claims and experiments, size of the codebase etc. I find it hard to believe the figures since the bottleneck in large codebases is not a compute, e.g. headers preprocessing, but it's a memory bandwidth.
SSD bandwidth: 4-10GB/s RAM bandwidth: 5-10x that, say 40GB/s.
If compute was not a bottleneck, the entire linux kernel should compile in less than 1 second.
So why does it take minutes to compile?
Compilation is entirely compute bound, the inputs and outputs are minuscule data sizes, in the order of megabytes for typical projects - maybe gigabytes for multi million line projects, but that is still only a second or two from an SSD.
Of course the above is specific to the machines I did my testing on. A different machine may have other differences from my setup. Still my experience matches the claim: at 40 cores memory bandwidth is the bottleneck not CPU speed.
Most people don't have 40+ core machines to play with, and so will not see those results. The machines I tested on cost > $10,000 so most would argue that is not affordable.
I’m not claiming anything about it being I/O or compute bound, but you are missing some sources of I/O:
- the compiler reads many source files (e.g. headers) multiple times
- the compiler writes and then reads lots of intermediate data
- the OS may have to swap out memory
Also, there may be resource contention that makes the system do neither I/O nor compute for part of the build.
Input: single .c file 8.5MB.
Output: 1.8MB object file.
Debug build took 1.5s.
Release build (O2) took about 6s.
That is about 3 orders of magntiude slower than what this machine is capable of in terms of IO from disk.
On an older 2 socket workstation, with relatively poor memory bandwidth, I ran a linux kernel compile.
perf stat --topdown --td-level 2
I also analyzed the memory bandwidth using
perf stat --per-socket -M memory_bandwidth_read,memory_bandwidth_write -a -r0 sleep 1
iostat indicates there are pretty much no reads/writes happening on the relevant disks.
Every core is 100% busy.
Core can be 100% busy but as I see you're a database kernel developer you must surely know that this can be an artifact of a stall in a memory backend of the CPU. I rest my case.
It's true across a wide range of projects. I build a lot of stuff from source and I routinely look at performance counters and other similar metrics to see what the bottlenecks are (I'm almost clinically impatient).
Building e.g. LLVM, a project with much longer per-translation unit build times, shows that memory bandwidth is even less of a bottleneck. Whereas fetch latency increased as a bottleneck.
> Core can be 100% busy but as I see you're a database kernel developer you must surely know that this can be an artifact of a stall in a memory backend of the CPU. I rest my case.
Hence my reference to doing a topdown analysis with perf. That provides you with a high-level analysis of what the actual bottlenecks are.
Typical compiler work (with typical compiler design) has lots of random memory accesses. Due to access latencies being what they are, that prevents you from actually doing enough memory accesses to reach a particularly high memory bandwidth.
Sorry, but compilation is simply not memory bandwidth bound. There are significant memory latency effects, but bandwidth != latency.
The system has well over 450GB/s of memory bandwidth.
LLVM peak is suspiciously low since building LLVM is heavier than the kernel? Anyway, on my machine, which is dual-socket 2x22-core skylake-x, for pure release build without debug symbols (less memory pressure), I get ~60GB/s.
# python do_pair_combined.py out_clang_release
Peak combined memory bandwidth found in block #180:
S0_write: 8046.8 MB/s
S0_read: 23098.2 MB/s
S1_write: 7611.3 MB/s
S1_read: 21231.3 MB/s
Total: 59987.6 MB/s
$ python do_pair_combined.py out_clang_relwithdeb
Peak combined memory bandwidth found in block #601:
S0_write: 11648.5 MB/s
S0_read: 17347.9 MB/s
S1_write: 31686.2 MB/s
S1_read: 37532.7 MB/s
Total: 98215.3 MB/s
$ python do_pair_combined.py out_kernel
Peak combined memory bandwidth found in block #329:
S0_write: 8963.9 MB/s
S0_read: 16584.1 MB/s
S1_write: 7863.4 MB/s
S1_read: 14371.0 MB/s
Total: 47782.399999999994 MB/s
$ python do_single.py out_clang_relwithdeb
Peak memory_bandwidth_write: 31686.2 MB/s
Peak memory_bandwidth_read: 52038.0 MB/s
So, I think it is not unreasonable to say that there are compiler workloads that can be limited by the memory bandwidth. I for sure worked with heavier codebases even than LLVM, and even though I did not do the measurements back then, the gut feeling I was having is that the bw is consumed. Some translation units would literally stay for few minutes "compiling" but no progress would have been made.
I agree that random access memory patterns and the latency those patterns incur are also a cost that need to be added to this cost function.
My initial comment on this topic was - I don't really believe that the bottleneck in compilation for larger codebases, of course not on _any_ given machine, is on the compute side, and therefore I don't see how modules are going to fix any of this.
Indeed! Compilation is notorious for being a classing pointer chasing load that is hard to brute force and a good way to benchmark overall single-thread core performance. It is more likely to be memory latency bound than memory bandwidth bound.
Edit: I think I misunderstood what you meant by memory bandwidth at first? Modules reduce the amount of work being done by the compiler in parsing and interpreting C++ code (think constexpr). Even if your compilation infrastructure is constrained by RAM access, modules replace a compute+RAM heavy part with a trivial amount of loading a module into compiler memory so it's a win.
source? language? what exactly does memory bandwidth have to do with compilation times in your example?
As a result, parsing and semantic analysis cannot be easily divided into independent parts to run in parallel, so they must be performed serially. A modern implementation will typically carry out semantic analysis in phases, for example binding names first, then analyzing types, and so on, before lowering the resulting representation to a form suitable for code generation.
Generally speaking, declarations that introduce names into non‑local scopes must be compiled serially. This also makes the symbol table a limiting factor for parallelism, since it must be accessed in a mutually exclusive manner. _Some_ constructs can be compiled in parallel, such as function bodies and function template instantiations, but given that build systems already implement per‑translation‑unit parallelism, the additional effort is often not worthwhile.
In contrast, a language like C# is designed with context‑free syntax. This allows a top‑level fast parse to break up the source file (there are no #include's in C#) into declarations that can, in principle, be processed in parallel. There will still be dependencies between declarations, and these will limit parallelism. But given that C# source files are a tiny fraction of the size of a typical C++ translation unit, even here parallel compilation is probably not a big win.
The C++ back-end can take advantage of multithreading far more than the front end. Once global optimizations are complete, the remaining work can be queued in parallel for code generation. MSVC works in exactly this way and provides options to control this parallelism. However, parallelism is limited by Amdahl’s Law, specifically the need to read in the IR generated by the front-end and to perform global optimizations.
NUMA (non-unifrom memory access - basically give each CPU a serpate bank of RAM, and if you need something that is in the other bank of RAM you need to ask the other CPU) exists because of this. I don't have access to a NUMA to see how they compare. My understanding (which could be wrong) is OS designers are still trying to figure out how to use them well, and they are not expected to do well for all problems.