Crates.io | alkomp |
lib.rs | alkomp |
version | 0.1.0 |
source | src |
created_at | 2020-11-04 22:02:36.178111 |
updated_at | 2020-11-04 22:02:36.178111 |
description | A compute library written in Rust with WebGPU |
homepage | |
repository | |
max_upload_size | |
id | 308747 |
size | 23,380 |
alkomp is a GPGPU library written in Rust for performing compute operations. It's designed to work over WebGPU, enabling compute code to work on DirectX, Vulkan, Metal, and eventually OpenCL and the browser.
Python bindings work around numpy
arrays, with an example provided below.
Create your project: cargo new --bin gpuproject
Add to cargo.toml
:
[dependencies]
alkomp = {git = "https://github.com/RustyBamboo/alkomp", branch = "main"}
Modify src/main.rs
.
As an example, this code runs the Collatz sequence on the GPU.
use alkomp;
fn main() {
let code = "
#version 450
layout(local_size_x = 1) in;
layout(set = 0, binding = 0) buffer PrimeIndices {
uint[] indices;
};
uint collatz_iterations(uint n) {
uint i = 0;
while(n != 1) {
if (mod(n, 2) == 0) {
n = n / 2;
}
else {
n = (3 * n) + 1;
}
i++;
}
return i;
}
void main() {
uint index = gl_GlobalInvocationID.x;
indices[index] = collatz_iterations(indices[index]);
}";
let mut spirv = alkomp::glslhelper::GLSLCompile::new(&code);
let shader = spirv.compile("main").unwrap();
let arr: Vec<u32> = vec![1, 2, 3, 4];
let mut device = alkomp::Device::new(0);
let data_gpu = device.to_device(arr.as_slice());
let args = alkomp::ParamsBuilder::new()
.param(Some(&data_gpu))
.build(Some(0));
let compute = device.compile("main", &shader, &args.0).unwrap();
device.call(compute, (arr.len() as u32, 1, 1), &args.1);
let collatz = futures::executor::block_on(device.get(&data_gpu)).unwrap();
let collatz = &collatz[0..collatz.len() - 1];
assert_eq!(&[0, 1, 7, 2], &collatz[..]);
}
In addition to writing Rust code, it is also possible to write Python code which interfaces with alkomp
. At this time, the Python interface is designed to specifically work with numpy ndarrays
. This means you can quickly send a numpy array to a GPU with data_gpu = device.to_device(my_np_array)
and run a computation using device.call(...)
. to_device
returns an object that records the memory location of a GPU buffer, as well shape and type. In order to retrieve the contents of the buffer: device.get(data_gpu)
. get
function returns a numpy in the same shape as my_np_array
.
To build the python library read this.
As an example, we do the same computation as above but with python:
#!python3
import alkompy
import numpy as np
arr = np.array(range(1,5), dtype=np.uint32)
# Retrieve a GPU device
dev = alkompy.Device(0)
# Send data to a GPU
data_gpu = dev.to_device(arr)
# GLSL code to compile
code = """
#version 450
layout(local_size_x = 1) in;
layout(set = 0, binding = 0) buffer PrimeIndices {
uint[] indices;
};
uint collatz_iterations(uint n) {
uint i = 0;
while(n != 1) {
if (mod(n, 2) == 0) {
n = n / 2;
}
else {
n = (3 * n) + 1;
}
i++;
}
return i;
}
void main() {
uint index = gl_GlobalInvocationID.x;
indices[index] = collatz_iterations(indices[index]);
}"""
shader = alkompy.compile_glsl(code)
# Run the shader and specifying the order of the bindings
dev.run("main", shader, (len(arr), 1, 1), [data_gpu])
result = dev.get(data_gpu)
assert((result == np.array([0, 1, 7, 2])).all())
Currently, compute kernel codes, which run on GPU, are not natively written in Rust. Shaderc is used to compile GLSL
to SPIR-V
.