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).

安装

将 "custos" 添加为依赖项

[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"]}

可用功能

示例

custos只实现了四种Buffer操作。这些操作是write、read、copy_slice和clear，然而，还有unary（仅设备）操作。
另一方面，[custos-math]实现了更多操作，包括自定义Matrix结构的矩阵操作。

为CPU实现操作：如果您想为所有计算设备实现自己的操作，请考虑查看这里：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
    }
}

更多使用示例可以在tests和examples文件夹中找到。（或者在unary操作文件、custos-math和sliced中）