ffi-overhead
krustlet
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ffi-overhead | krustlet | |
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19 | 21 | |
639 | 3,530 | |
- | 0.3% | |
0.0 | 3.1 | |
10 months ago | 7 months ago | |
C | Rust | |
Apache License 2.0 | Apache License 2.0 |
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ffi-overhead
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3 years of fulltime Rust game development, and why we're leaving Rust behind
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?
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Can Fortran survive another 15 years?
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.
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When dealing with C, when is Go slow?
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
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Understanding N and 1 queries problem
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
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Are there plans to improve concurrency in Rust?
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).
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Comparing the C FFI overhead on various languages
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.
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Would docker be faster if it were written in rust?
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.
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Using Windows API in Julia?
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 :).
krustlet
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WASM Instructions
Oh it’s certainly looking like that IMO.
You can run wasm in k8s: https://krustlet.dev/
Docker itself can run wasm: https://wasmlabs.dev/articles/docker-without-containers/
There are a few serverless runtimes based on wasm: https://wasmcloud.com/
A lot of those are powered by wasmtime or WasmEdge.
If you’re wanting to be able to just pull down a random app and run it as wasm, that’s inherently harder with wasm, because you have to recompile, and amazing compiling stuff is always harder than it should be. For example I compiled jq to wasm to other day, so you dont have to worry (as much) about the CVEs that was issued recently. https://github.com/rockwotj/jq-wasi
- The advantage of WASM compared with container runtimes
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Crafting container images without Dockerfiles
It can, kubevirt is a project for running VMs https://kubevirt.io/ and there have been more esoteric things like WASM (https://github.com/krustlet/krustlet).
- The Python Paradox
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I Don’t wanna use Docker or kubernetes
Or you can run Krustlet instead of Kubelet. That makes it so you can only run WebAssembly on the cluster - so no Go, no Python, only Rust!
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Why did the Krustlet project die?
But the project seems to have died: https://github.com/krustlet/krustlet/graphs/contributors
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Does anybody have a use-case for Scala WASM compilation target?
There are some cloud providers that are starting to offer wasm support. Docker is currently working on wasm https://docs.docker.com/desktop/wasm/ There is also krustlet https://krustlet.dev/ which lets you run wasm in kubernetes
- How I got involved in the Rust community
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Are V8 isolates the future of computing?
> If one writes Go or Rust, there are much better ways to run them than targeting WASM
wasm has its place, especially for contained workloads that can be wrapped in its strict capability boundaries (think, file-encoding jobs that shouldn't access anything else but said files: https://news.ycombinator.com/item?id=29112713).
> Containers are still the defacto standard.
wasmedge [0], atmo [1], krustlet [2], blueboat [3] and numerous other projects are turning up the heat [4]!
[0] https://github.com/WasmEdge/WasmEdge
[1] https://github.com/suborbital/atmo
[2] https://github.com/krustlet/krustlet
[3] https://github.com/losfair/blueboat
[4] https://news.ycombinator.com/item?id=30155295
- Krustlet: Kubernetes Kubelet in Rust for Running WASM
What are some alternatives?
go - The Go programming language
miniflare - 🔥 Fully-local simulator for Cloudflare Workers. For the latest version, see https://github.com/cloudflare/workers-sdk/tree/main/packages/miniflare.
sqlite
youki - A container runtime written in Rust
glmark2 - glmark2 is an OpenGL 2.0 and ES 2.0 benchmark
yew - Rust / Wasm framework for creating reliable and efficient web applications
kutil - Go Utilities
brython - Brython (Browser Python) is an implementation of Python 3 running in the browser
lzbench - lzbench is an in-memory benchmark of open-source LZ77/LZSS/LZMA compressors
Transcrypt - Python 3.9 to JavaScript compiler - Lean, fast, open! -
CheeseShop - Examples of using PyO3 Rust bindings for Python with little to no silliness.
awesome-paas - A curated list of PaaS, developer platforms, Self hosted PaaS, Cloud IDEs and ADNs.