ffi-overhead VS win32metadata

The overhead for Go in benchmarks is insane in contrast to other languages - https://github.com/dyu/ffi-overhead Are there reasons why Go does not copy what Julia does?

What about the other benchmarks on the same site? https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Bio/BCR/ BCR takes about a hundred seconds and is pretty indicative of systems biological models, coming from 1122 ODEs with 24388 terms that describe a stiff chemical reaction network modeling the BCR signaling network from Barua et al. Or the discrete diffusion models https://docs.sciml.ai/SciMLBenchmarksOutput/stable/Jumps/Dif... which are the justification behind the claims in https://www.biorxiv.org/content/10.1101/2022.07.30.502135v1 that the O(1) scaling methods scale better than O(log n) scaling for large enough models? I mean.

> If you use special routines (BLAS/LAPACK, ...), use them everywhere as the respective community does.

It tests with and with BLAS/LAPACK (which isn't always helpful, which of course you'd see from the benchmarks if you read them). One of the key differences of course though is that there are some pure Julia tools like https://github.com/JuliaLinearAlgebra/RecursiveFactorization... which outperform the respective OpenBLAS/MKL equivalent in many scenarios, and that's one noted factor for the performance boost (and is not trivial to wrap into the interface of the other solvers, so it's not done). There are other benchmarks showing that it's not apples to apples and is instead conservative in many cases, for example https://github.com/SciML/SciPyDiffEq.jl#measuring-overhead showing the SciPyDiffEq handling with the Julia JIT optimizations gives a lower overhead than direct SciPy+Numba, so we use the lower overhead numbers in https://docs.sciml.ai/SciMLBenchmarksOutput/stable/MultiLang....

> you must compile/write whole programs in each of the respective languages to enable full compiler/interpreter optimizations

You do realize that a .so has lower overhead to call from a JIT compiled language than from a static compiled language like C because you can optimize away some of the bindings at the runtime right? https://github.com/dyu/ffi-overhead is a measurement of that, and you see LuaJIT and Julia as faster than C and Fortran here. This shouldn't be surprising because it's pretty clear how that works?

I mean yes, someone can always ask for more benchmarks, but now we have a site that's auto updating tons and tons of ODE benchmarks with ODE systems ranging from size 2 to the thousands, with as many things as we can wrap in as many scenarios as we can wrap. And we don't even "win" all of our benchmarks because unlike for you, these benchmarks aren't for winning but for tracking development (somehow for Hacker News folks they ignore the utility part and go straight to language wars...).

If you have a concrete change you think can improve the benchmarks, then please share it at https://github.com/SciML/SciMLBenchmarks.jl. We'll be happy to make and maintain another.

If you're calling back and forth between C and Go in a performance critical way. It's one of the slowest languages for wrapping C that there is. I've personally hit this bottleneck in numerous projects, wrapping things like libutp and sqlite. See also https://github.com/dyu/ffi-overhead

Piling on about overhead (and SQLite), many high-level languages take some hit for using an FFI. So you're still incentivized to avoid tons of SQLite calls.

https://github.com/dyu/ffi-overhead

Go doesn't even have native thread stacks. When call any FFI function Go has to switch over to an on-demand stack and coordinate the goroutine and the runtime to avoid preemption and starvation. This is part of why Go's calling overhead is over 30x slower than C/C++/Rust (source). It's understandbly become Go community culture to act like FFI is just not even an option and reinvent everything in Go, but that reinvented Go suffers from these other problems plus many more (such as optimizing far worse than GCC or LLVM).

Some of the results look outdated. The Dart results look bad (25x slower than C), but looking at the code (https://github.com/dyu/ffi-overhead/tree/master/dart) it appears to be five years old. Dart has a new FFI as of Dart 2.5 (2019): https://medium.com/dartlang/announcing-dart-2-5-super-charge... I'm curious how the new FFI would fare in these benchmarks.

In that case, the libcontainer library would be faster if written in most other languages seeing as Go has unfortunate C-calling performance. In this FFI benchmark Rust is on par with C with 1193ms (total benchmarking time), while Go took 37975ms doing the same.

Hi there folks! I'm going to call the Windows API as rapidly as possible and will be doing some calculations with the results, and I thought Julia might be perfect for this task as its FFI is impressively fast, and of course, Julia is fast regarding numbers as well :).

As /u/huellenoperator notes, that this needs a pointer to a mutable string comes straight from microsoft through win32metadata. Maybe it's a mistake on Microsoft's side, but if it's not you're taking big risks.

Microsoft offers official "bindings" to Win32 APIs through win32metadata. However, until recently, it did not include metadata for kernel-level functions or WDK. In early 2021, an issue was raised through windows-rs regarding this limitation, but progress was slow until now. Microsoft has finally released official metadata for WDK, which can be found on the wdkmetadata repository. The latest comment on the issue thread can be found here:

for win32metadata's kernel api tracking issue, https://github.com/microsoft/win32metadata/issues/401

Any examples? Since the API bindings in windows-sys are generated from the metadata generated from official Windows SDK headers I'd not expect to see this kind of difference.

You might also look at this project: https://github.com/microsoft/win32metadata

There is still the occasional incredibly subtle link time fuckery in Rust.

https://github.com/microsoft/win32metadata/issues/1274

"Minor" semver updates to crates breaking things via e.g. unexpected MSRV bumps is pretty common too, with some resulting bitrot. That said, I agree with you that things in Rust are at least better. Imperfect, but better.

Win32 is available as metadata to enable adoption in as many languages as possible. Are there some things missing? Yes. The Microsoft team acknowledges that and encourages asking for the things you need so they can add them to the metadata.

It might be interesting to have bindings generated for the entirety of Win32 API through https://github.com/microsoft/win32metadata

Hi JB! Funny to cross paths with you in this context. I don't know if you remember me but I was a rookie programmer who got the pleasure of joining the VideoLan Conference in Dublin back in 2014, and then Paris the next year, and you were very kind to me.

The GitHub issue title here is unfortunately misleading. I have renamed it to "ideas to improve windows header files and libc". Also, I hope it is clear that I rebutted the points made by the OP, because I completely agree with your summary that the mingw-w64 people are skilled, nice and very clever and think about all use cases.

If any drive-by HN readers work at Microsoft, please help us with this issue: https://github.com/microsoft/win32metadata/issues/766