wizer
cheerp-meta
wizer | cheerp-meta | |
---|---|---|
10 | 2 | |
972 | 1,079 | |
1.3% | 1.5% | |
7.6 | 5.7 | |
5 months ago | 8 months ago | |
Rust | JavaScript | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
wizer
-
RustPython
> once by the wasm runtime to compile the rust-python wasm
I'm not sure what you mean by that. The runtime doesn't compile WASM, it simply executes it.
There are tools for dealing with interpreter runtime overhead this by pre-initalizing the environment like Wizer[0]. ComponentizeJS[1] uses it to pre-initialize the Spidermoney engine it packages to gain fast startup times (and you can then prune the initialization only code with wasm-opt). As techniques like ComponentizeJS are also being applied for a specific set of interpreted files, you can even prune parts of the interpreter that would never be used for that specific program. If you want to go even further you could record specific execution profiles and optimize further by those.
[0]: https://github.com/bytecodealliance/wizer
[1]: https://github.com/bytecodealliance/ComponentizeJS
- Are V8 isolates the future of computing?
-
Netlify Edge Functions: A new serverless runtime powered by Deno
Edge functions are typically run intermittently, with their runtime stopped to free up resources between runs. Therefore a big factor is startup and shutdown speed. Containers are pretty bad there. Deno is better, and WASM is unbeatable, especially with things like Wizer[0].
[0]https://github.com/bytecodealliance/wizer
-
Building a WebAssembly-powered serverless platform
I imagine startup cost could be amortized by something like wizer: https://github.com/bytecodealliance/wizer
-
Containerless! How to Run WebAssembly Workloads on Kubernetes with Rust
There are security benefits to running each request in its own instance, as it helps prevent accidental leaking of state between requests. To avoid doing lots of expensive initializations, we have a tool called wizer which lets users run their program's initialization once, create a snapshot, and then use that snapshot to do fast startups that don't rerun the whole initialization each time.
-
Is it possible in Rust to save the complete state of a program and restore it later? Such as may be accomplished in some implementations of Common Lisp
See https://github.com/bytecodealliance/wizer for an implementation of this approach.
-
Bytecode Alliance
It should probably be named "Making JavaScript to startup fast on WebAssembly", since the runtime speed is not really improved by the approach they exposed.
Besides that I think Wizer [1] is both an elegant and a simple solution to speed up startup speed with Wasm.
[1] - https://github.com/bytecodealliance/wizer#using-wizer-as-a-l...
-
A JavaScript optimizing compiler
A similar project, for WebAssembly so with limited scope is this: https://github.com/bytecodealliance/wizer. And somehow similar but limited on LLVM IR a colleague worked on this for Cheerp (the compiler used here as backend): https://github.com/leaningtech/cheerp-meta/wiki/Cheerp-PreExecuter.
- Wizer: snapshot an initialized Wasm instance and save the result as a new, pre-initialized Wasm module. Up to 6x faster start up on my test workloads
- Wiser: snapshot an initialized Wasm instance and save the result as a new, pre-initialized Wasm module. Up to 6x faster start up on my test workloads
cheerp-meta
-
Ask HN: Real-world examples of WASM usage
Yours is a fair question. I think that, right now, adoption of WebAssembly is quite limited. On the other hand (here at LeanigTech) we are extremely bullish about its potential.
We believe that this technology would be adopted more with better tooling. Our main contribution to this space is Cheerp: A C++-to-WebAssembly _and_ JavaScript compiler (https://github.com/leaningtech/cheerp-meta/). It is designed to seamlessly take advantage of Wasm without sacrificing easy access to Web APIs, all from within C++ with no need of post-processing and glue code.
We know for a fact that amazing products can be build with Cheerp, because we have done it ourselves.
CheerpX is a x86 virtual machine running in the browser, fully written in C++ and compiled with Cheerp. It includes a JIT-compiler that is able to analyze x86 binary code and emit new WebAssembly modules on the fly.
Our most impressive demo yet (WebVM) is available here: https://webvm.io/
-
A JavaScript optimizing compiler
A similar project, for WebAssembly so with limited scope is this: https://github.com/bytecodealliance/wizer. And somehow similar but limited on LLVM IR a colleague worked on this for Cheerp (the compiler used here as backend): https://github.com/leaningtech/cheerp-meta/wiki/Cheerp-PreExecuter.
What are some alternatives?
quickjs-emscripten - Safely execute untrusted Javascript in your Javascript, and execute synchronous code that uses async functions
EmGlue - 🕸️ Glue C++ to your browser! Universal bindings for JavaScript/Wasm using Glue and Embind.
TablaM - The practical relational programing language for data-oriented applications
PSI - Private Set Intersection Cardinality protocol based on ECDH and Bloom Filters
wagi - Write HTTP handlers in WebAssembly with a minimal amount of work
clang-wasm - How to build webassembly files with nothing other than standard Clang/llvm.
go-wasm-bake - Experimenting with eager evaluation of Go WASM code
walt - :zap: Walt is a JavaScript-like syntax for WebAssembly text format :zap:
wasmtime - A lightweight WebAssembly runtime that is fast, secure, and standards-compliant
obs-studio-node - libOBS (OBS Studio) for Node.Js, Electron and similar tools
go - The Go programming language
perspective - A data visualization and analytics component, especially well-suited for large and/or streaming datasets.