Lib.rs

A minimal OpenCL, WGPU, CUDA and host CPU array manipulation engine / framework written in Rust. This crate provides the tools for executing custom array and automatic differentiation operations with the CPU, as well as with CUDA, WGPU and OpenCL devices.
This guide demonstrates how operations can be implemented for the compute devices: implement_operations.md
or to see it at a larger scale, look here custos-math or here sliced (for automatic diff examples).

Installation

Add "custos" as a dependency:

[dependencies]
custos = "0.7.0"

# to disable the default features (cpu, cuda, opencl, static-api, blas, macro) and use an own set of features:
#custos = {version = "0.7.0", default-features=false, features=["opencl", "blas"]}

Available features:

Examples

custos only implements four Buffer operations. These would be the write, read, copy_slice and clear operations, however, there are also unary (device only) operations.
On the other hand, [custos-math] implements a lot more operations, including Matrix operations for a custom Matrix struct.

Implement an operation for CPU: If you want to implement your own operations for all compute devices, consider looking here: implement_operations.md

use std::ops::Mul;
use custos::prelude::*;

pub trait MulBuf<T, S: Shape = (), D: Device = Self>: Sized + Device {
    fn mul(&self, lhs: &Buffer<T, D, S>, rhs: &Buffer<T, D, S>) -> Buffer<T, Self, S>;
}

impl<T, S, D> MulBuf<T, S, D> for CPU
where
    T: Mul<Output = T> + Copy,
    S: Shape,
    D: MainMemory,
{
    fn mul(&self, lhs: &Buffer<T, D, S>, rhs: &Buffer<T, D, S>) -> Buffer<T, CPU, S> {
        let mut out = self.retrieve(lhs.len(), (lhs, rhs));

        for ((lhs, rhs), out) in lhs.iter().zip(&*rhs).zip(&mut out) {
            *out = *lhs * *rhs;
        }

        out
    }
}

A lot more usage examples can be found in the tests and examples folders. (Or in the unary operation file, custos-math and sliced)