emscripten VS compute-shader-101

Browsers limit the ability for these platforms to use raw sockets, there simply is no API for it. The best that can be done /today/ is to use WebSockets, which are not the same thing any can't be used for HTTP requests without the server expecting a WebSocket connection:

https://github.com/emscripten-core/emscripten/issues/5196#is...

Only half true. Emscripten implements the SDL 1.2 (and also SDL_mixer 1.2) API in Javascript here: https://github.com/emscripten-core/emscripten/blob/main/src/.... On the other hand SDL 2 (and SDL_mixer 2) are proper ports (which you linked to).

So there's quite a size penalty to using SDL 2 rather than SDL 1.2.

Playing with low-level stuff is fun, but I won't use it anywhere in productionable code. Well, at least without considerable experience and understanding of the Emscripten code base.

Thank you for your comment!

WASM gave Figma a lot of speed by default for a lot of perf-sensitive code like rendering, layouts, applying styles and materializing component instances, our GUI code is mostly React and CSS.

WASM engine performance has not been a problem for us, instead we are constantly looking forward improvements in the devex department: debugging, profiling and modularization.

One of the largest challenges of the platform we face today is the heap size limit. While Chrome supports up to 4GB today, that's not yet the case for all browsers. And even with that, we are still discovering bugs in the toolchain (see this recent issue filed by one of our engineers) https://github.com/emscripten-core/emscripten/issues/20137

The challenge of the perf-testing at scale in our company is helping developers to detect perf regressions when they don't expect them - accidental algorithmic errors, misused caches, over-rendering React components, dangerously inefficient CSS directives, etc.

https://github.com/emscripten-forge/recipes/tree/main/recipe...

Re: emscripten fs implementations: https://github.com/emscripten-core/emscripten/issues/15041#i... https://github.com/jupyterlite/jupyterlite/issues/315

Moving onto the project, let’s first install Emscripten from their git repository. Emscripten will compile C into Wasm code. An important note is that I will be using Mac OS for this project. If you want to follow along using Windows, use this link. To Begin, open your terminal and clone down Emscripten with:

I got pointed to this one here: https://github.com/emscripten-core/emscripten/issues/15487

If you want to do it, go to town. This is the only reference I could find to anybody targeting wasm with i2pd frankly I really hope the person inquiring in the issue is also you.

Right, and if that momentum is going to last 20 years, then that's going to dictate what's a good strategic decision and what isn't for the next 20 years. Thanks for letting me know what the good options might be when my infant daughter is halfway through college, that doesn't help me save for it in the meantime.

Prior familiarity is a very good reason to pick an option for a greenfield project if you're operating on any type of serious budget (time or money), especially if you need to hire others to help out. There's also the annoying reality that most libraries for new/up-and-coming languages are simply inadequate, despite whatever claims they make.

For instance, one of my personal side projects involves getting familiar with WebAssembly (note: not on a serious budget), and I'm using emscripten to transcode because that's what the internet seemed to think was the closest thing to a standard toolkit. I found a bug simply by combining two pieces of example code from Emscripten's own documentation (you'll note I'm transcoding from c++ due to prior familiarity): https://github.com/emscripten-core/emscripten/issues/17143

This is not a dig on emscripten per se, merely pointing out that it's probably not mature enough for the stress of corporate-scale development, where you encounter all sorts of crazy edge cases well beyond the sample code. Java over the years has obtained that level of maturity. The edge cases are largely solved or at least known, and there's an army of experts and consultants ready to help if there's a problem. When time is money, that matters. It determines the risk profile of any project, greenfield or otherwise.

Java's momentum hasn't stopped, at best it's simply slowing. And at this rate it'll take decades to come to a stop, and decades more to recede to any meaningful degree. I'll also point out that C is very much alive and well in the embedded world. Plenty of job postings looking for C experience explicitly.

You might find compute shader 101 useful.

Compute Shader 101 - Github, Video, Slideshow. additional resources at end of slide show.

Yes, compute-shader-101 is sample code + video + slides.

Workgroup in Vulkan/WebGPU lingo is equivalent to "thread block" in CUDA speak; see [1] for a decoder ring.

> Using atomics to solve this is rarely a good idea, atomics will make things go slowly, and there is often a way to restructure the problem so that you can let threads read data from a previous dispatch, and break your pipeline into more dispatches if necessary.

This depends on the exact workload, but I disagree. A multiple dispatch solution to prefix sum requires reading the input at least twice, while decoupled look-back is single pass. That's a 1.5x difference if you're memory saturated, which is a good assumption here.

The Nanite talk (which I linked) showed a very similar result, for very similar reasons. They have a multi-dispatch approach to their adaptive LOD resolver, and it's about 25% slower than the one that uses atomics to manage the job queue.

Thus, I think we can solidly conclud that atomics are an essential part of the toolkit for GPU compute.

You do make an important distinction between runtime and development environment, and I should fix that, but there's still a point to be made. Most people doing machine learning work need a dev environment (or use Colab), even if they're theoretically just consuming GPU code that other people wrote. And if you do distribute a CUDA binary, it only runs on Nvidia. By contrast, my stuff is a 20-second "cargo build" and you can write your own GPU code with very minimal additional setup.

[1]: https://github.com/googlefonts/compute-shader-101/blob/main/...

googlefonts/compute-shader-101: Sample code for compute shader 101 training (github.com)

I agree strongly with you about the need for good resources. Here are a few I've found that are useful.

* A trip through the Graphics Pipeline[1] is slightly dated (10 years old) but still very relevant.

* If you're interested in compute shaders specifically, I've put together "compute shader 101"

* Alyssa Rosenzweig's posts[3] on reverse engineering GPUs casts a lot of light on how they work at a low level. It helps to have a big-picture understanding first.

I think there is demand for a good book on this topic.

[1]: https://fgiesen.wordpress.com/2011/07/09/a-trip-through-the-...

[2]: https://github.com/googlefonts/compute-shader-101

[3]: https://rosenzweig.io/

This is a talk I've been working on for a while. It starts off motivating why you might want to write compute shaders (tl;dr you can exploit the impressive compute power of GPUs but portably), then explains the basics of how, including some sample code to help get people started.

Slides: https://docs.google.com/presentation/d/1dVSXORW6JurLUcx5UhE1...

Sample code: https://github.com/googlefonts/compute-shader-101

Feedback is welcome (please file issues against the open source repo), and AMA in this thread.