phaser VS ffi-overhead

If you're targeting the browser first why not use a browser first library like PhaserJS [0]?. I don't see a reason to work around with WASM; HTML5 canvas might be everything that you need.

[0] https://phaser.io/

Gold : GitHub, Phaser Studio, Arcadia

Versatility: JavaScript is not limited to web browsers. It's used in a variety of environments, including mobile app development (using frameworks like React Native), game development (using libraries like Phaser), and even serverless computing (using platforms like AWS Lambda).

This reminds me of my first real dev job, 10y ago, making small facebook games with https://phaser.io it was actually kind of fun now that I think back.

Is it worth it? I think while attempting to create a game engine with the Canvas API and vanilla JavaScript. (I quickly ditched that idea and started using PhaserJS)

I didn't try to build anything with Phaser, but I evaluated it a bit when trying to pick a game engine for a 2D web game.

The tech didn't impress me that much, but it also seemed like the most mature 2D game engine available in JS.

Notably, Phaser 4 was announced ~four years ago and was an attempt to get the project written natively in TypeScript. It looks pretty dead in the water - https://github.com/phaserjs/phaser and having a "best effort" TypeScript experience layered onto Phaser 3 didn't excite me.

Additionally, with browsers gaining support for WebGPU, I expect any game engine worth their snuff to begin rapidly adopting support for WebGPU. As best I can tell, any hope of Phaser supporting WebGPU is lumped into Phaser 4, so... not much to say there.

Overall, it was a little tough for me to tell if I was being overly critical and viewing a mature product as a ghost town, but that's the impression I took away from it.

As far as I can tell, BabylonJS is king in town for a TypeScript game engine, but its focus is 3D experiences. I didn't find an especially compelling 2D game engine. I ended up making a prototype using React + PixiJS + React-Pixi, but that was hardly an engine and had significant performance issues.

Now I am building in Rust with Bevy. It's slow going, creating UI elements sucks right now, but the underlying tech is super solid and I feel good about what I write and what I learn even if I'm dismayed at the pace in which I am creating.

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 :).