qbe-rs VS Befunge

There is likely no general anwer to this question. LLVM and WASM are sufficiently different technologies for different purposes. The original purpose of WASM was to enable code of statically compiled languages like C++ or Rust to run in the browser, whereas LLVM is a huge framework mostly useful as a compiler backend for a multitude of architectures. I'm aware that WASM is more and more used as a general purpose managed runtime (like the .NET ECMA-335 Common Language Infrastructure). And don't forget that there are much leaner, but still decent alternatives to LLVM, such as QBE (https://c9x.me/compile/) or ECS (https://github.com/EigenCompilerSuite/). So the answer to your question heavily depends on what you actually want to implement.

Another problem with LLVM I’ve heard about is that it’s intermediate language or API or something is a moving, informally-specified target. People who know LLVM internals might weigh in on that claim. If true, it’s actually easier to target C or a subset of Rust just because it’s static and well-understood.

Two projects sought to mitigate these issues by going in different directions. One was a compiler backend that aimed to be easy to learn with well-specified IL. The other aimed to formalize LLVM’s IL.

http://c9x.me/compile/

https://github.com/AliveToolkit/alive2

There have also been typed, assembly languages to support verification from groups like FLINT. One can also compile language-specific analysis with a certified to LLVM IL compiler. Integrating pieces from different languages can have risks. That (IIRC) is being mitigated by people doing secure, abstract compilation.

> I think it uses a different backend than LLVM

harec uses https://c9x.me/compile/

Have you considered QBE?

I really hope other new projects (like QBE) can really grow and become widely used

I really liked targetting QBE (https://c9x.me/compile/) as an IR, as it gave me lots of back-end optimisations for free 😊.

There is: QBE.

You might be underestimating the accuracy of the CPU models LLVM uses.

For x86, the same data the code generator uses drives llvm-mca[1], which given a loop body can tell you the throughput, latency, and microarchitectural bottlenecks (decoding, ports, dependencies, store forwarding, etc.)—if not always precisely, then still not worse then IACA, the tool written at Intel by people who presumably knew how the CPUs work, unlike LLVM contributors and the rest of us who can only guess and measure. This separately for Haswell, Sandy Bridge, Skylake, etc.; not “x86”.

Now, is this the best model you can get? Not exactly[2], but it’s close enough to not matter. Do we often need machine code to be optimized to that level of detail? Perhaps not[3], and with that in mind you can shave at least a factor of ten off LLVM’s considerable bulk at the cost of 20—30% of performance[4,5]. But if you do want those as well, it seems that the complexity of LLVM is a fair price, or has the right order of magnitude at least.

(Frontend not included, C++ frontend required to bootstrap sold separately, at a similar markup compared to a C-only frontend with somewhat worse ergonomics.)

[1] https://llvm.org/docs/CommandGuide/llvm-mca.html

[2] https://www.uops.info/

[3] https://briancallahan.net/blog/20211010.html

[4] https://c9x.me/compile/

[5] https://drewdevault.com/talks/qbe.html

1. C compilers don't do a good job, & thus even CPython, which has historically stuck to rather vanilla C, uses computed goto, as described in https://eli.thegreenplace.net/2012/07/12/computed-goto-for-e...

I resorted to similar techniques in optimizing Befunge: https://github.com/serprex/Befunge (See bejit.c & marsh.c/marsh.h)

2. Rust enums are not variable sized, think of them as tagged C unions, where the Rust compiler can sometimes apply tricks to make Option> the same size as Vec

3. match can specialize for straight forward cases, when in doubt use https://godbolt.org

I've found async to be straight forward anytime I've used it. Promise#then is equivalent to callbacks

async/await often requires very little changes compared to synchronous code, whereas reworking a program into callbacks is much more impactful. & the async/await compilation process tends to produce better performance in addition to this. My first async/await work was a few years ago to increase a data importer's performance by an order of magnitude compared to the blocking code

Here's an example where looping made for a callback that recursively called, using async/await I get to use a plain loop:

before: https://github.com/serprex/Befunge/blob/946ea0024c4d87a1b75d...

after: https://github.com/serprex/Befunge/blob/9677ddddb7a26b7a17dd...

I don't see why people find it so complicated to separate begin-compute & wait-on-compute

I've since rewritten a nodejs game server into rust, https://github.com/serprex/openEtG/tree/master/src/rs/server... handleget/handlews are quite straight forward

You'll also find them used in CPython's ceval.c

I use them in both my C befunge implementations:

https://github.com/serprex/Befunge/blob/c97c8e63a4eb262f3a60...

https://github.com/serprex/Befunge/blob/c97c8e63a4eb262f3a60...