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The old version of Emu (which used macros) ishere.

Emu is a GPGPU library for Rust with a focus on portability, modularity, and performance.

It's a CUDA-esque compute-specific abstraction overWebGPU providing specific functionality to make WebGPU feel more like CUDA. Here's a quick run-down of highlight features...

• Emu can run anywhere - Emu uses WebGPU to support DirectX, Metal, Vulkan (and also OpenGL and browser eventually) as compile targets. This allows Emu to run on pretty much any user interface including desktop, mobile, and browser. By movingheavy computations to the user's device, you can reduce system latency and improve privacy.

• Emu makes compute easier - Emu makes WebGPU feel like CUDA. It does this by providing...

  • DeviceBox<T> as a wrapper for data that lives on the GPU (thereby ensuring type-safe data movement)
  • DevicePool as a no-config auto-managed pool of devices (similar to CUDA)
  • trait Cache - a no-setup-required LRU cache of JITed compute kernels.

• Emu is transparent - Emu is a fully transparent abstraction. This means, at any point, you can decide to remove the abstraction and work directly with WebGPU constructs with zero overhead. For example, if you want to mix Emu with WebGPU-based graphics, you can do that with zero overhead. You can also swap out the JIT compiler artifact cache with your own cache, manage the device pool if you wish, and define your own compile-to-SPIR-V compiler that interops with Emu.

• Emu is asynchronous - Emu is fully asynchronous. Most API calls will be non-blocking and can be synchronized by calls toDeviceBox::get when data is read back from device.

Here's a quick example of Emu. You can find more inemu_core/examples and most recent documentationhere.

First, we just import a bunch of stuff

use emu_glsl::*;use emu_core::prelude::*;use zerocopy::*;

We can define types of structures so that they can be safely serialized and deserialized to/from the GPU.

#[repr(C)]#[derive(AsBytes,FromBytes,Copy,Clone,Default,Debug)]structRectangle{x:u32,y:u32,w:i32,h:i32,}

For this example, we make this entire function async but in reality you will only want small blocks of code to be async (like a bunch of asynchronous memory transfers and computation) and these blocks will be sent off to an executor to execute. You definitely don't want to do something like this where you are blocking (by doing an entire compilation step) in your async code.

fnmain() ->Result<(),Box<dyn std::error::Error>>{    futures::executor::block_on(assert_device_pool_initialized());// first, we move a bunch of rectangles to the GPUletmut x:DeviceBox<[Rectangle]> =vec![Default::default();128].as_device_boxed()?;// then we compile some GLSL code using the GlslCompile compiler and// the GlobalCache for caching compiler artifactslet c =compile::<String,GlslCompile,_,GlobalCache>(GlslBuilder::new().set_entry_point_name("main").add_param_mut().set_code_with_glsl(r#"#version 450layout(local_size_x = 1) in; // our thread block size is 1, that is we only have 1 thread per blockstruct Rectangle {    uint x;    uint y;    int w;    int h;};// make sure to use only a single set and keep all your n parameters in n storage buffers in bindings 0 to n-1// you shouldn't use push constants or anything OTHER than storage buffers for passing stuff into the kernel// just use buffers with one buffer per bindinglayout(set = 0, binding = 0) buffer Rectangles {    Rectangle[] rectangles;}; // this is used as both input and output for convenienceRectangle flip(Rectangle r) {    r.x = r.x + r.w;    r.y = r.y + r.h;    r.w *= -1;    r.h *= -1;    return r;}// there should be only one entry point and it should be named "main"// ultimately, Emu has to kind of restrict how you use GLSL because it is compute focusedvoid main() {    uint index = gl_GlobalInvocationID.x; // this gives us the index in the x dimension of the thread space    rectangles[index] = flip(rectangles[index]);}            "#,))?.finish()?;// we spawn 128 threads (really 128 thread blocks)unsafe{spawn(128).launch(call!(c,&mut x));}// this is the Future we need to block on to get stuff to happen// everything else is non-blocking in the API (except stuff like compilation)println!("{:?}", futures::executor::block_on(x.get())?);Ok(())}

And last but certainly not least, we use an executor to execute.

fnmain(){    futures::executor::block_on(do_some_stuff()).expect("failed to do stuff on GPU");}

Emu is relatively new but has already been used for GPU acceleration in a variety of projects.

• Used intoil for GPU-accelerated linear algebra
• Used inipl3hasher for hash collision finding
• Used inbigbang for simulating gravitational acceleration (used older version of Emu)

The latest stable version ison Crates.io. To start using Emu, simply add the following line to yourCargo.toml.

[dependencies]emu_core ="0.1.1"

To understand how to start using Emu, check outthe docs. If you have any questions, pleaseask in the Discord.

Feedback, discussion, PRs would all very much be appreciated. Some relatively high-priority, non-API-breaking things that have yet to be implemented are the following in rough order of priority.

• Enusre that WebGPU polling is done correctly in `DeviceBox::get
• Add support for WGLSL as input, useNaga for shader compilation
• Add WASM support inCargo.toml
• Add benchmarks`
• Reuse staging buffers between differentDeviceBoxes
• Maybe use uniforms forDeviceBox<T> whenT is small (maybe)

If you are interested in any of these or anything else, please don't hesitate to open an issue on GitHub or discuss moreon Discord.