Ndarray CUDA Matrix Operations

Welcome to the Ndarray CUDA Matrix Operations library, a high-performance computing solution designed to accelerate matrix operations using Nvidia's CUDA technology with Rust's ndarray data structure. This library leverages the powerful cuBLAS library to perform efficient matrix multiplications on compatible Nvidia GPUs.

For ideas and methods of creating this library, please refer to:

Improving ndrray matrix computing performance with CUDA

Features

• Seamless integration with Rust's ndarray crate.
• High-performance matrix operations utilizing CUDA.
• Support for one-dimensional and two-dimensional arrays.
• Automatic memory management between host and device.
• Simple and intuitive API mirroring that of ndarray.

Prerequisites

To use this library, you will need:

• g++ 7 or above
• rustc 1.77.2
• cuda 10 or above

Usage

First, ensure that you have initialized the CUDA environment by calling init_cublas() before any matrix operations, and call destory_cublas() to clean up resources upon completion:

extern crate ndarray_cuda_matmul;

use ndarray_cuda_matmul::{init_cublas, destory_cublas};

fn main() {
    // Initialize cublas context
    init_cublas();

    // Your matrix operations here

    // Clean up cublas context
    destory_cublas();
}

To perform matrix multiplication, use the cuda_dot method provided by the trait CudaDot implemented for ndarray’s ArrayBase:

use ndarray::Array;
use ndarray_cuda_matmul::CudaDot;

let a = Array::from_shape_vec((m, k), vec![...]).unwrap();
let b = Array::from_shape_vec((k, n), vec![...]).unwrap();

let result = a.cuda_dot(&b);

Here m, n, and k represent the dimensions of the matrices, and vec![...] should be replaced with your actual data.

Using the method of first copying the matrix into GPU memory, here's a code example

let a = array![[1.0_f32, 2.0_f32, 3.0_f32], [4.0_f32, 5.0_f32, 6.0_f32]];
let b = array![[1.0_f32, 2.0_f32], [3.0_f32, 4.0_f32], [5.0_f32, 6.0_f32]];
let c = array![[1.0f32,1.0f32],[1.0f32,1.0f32]];
init_cublas();
let out = a.to_device().dot(&b.to_device()).dot(&c.to_device()).to_host();
destory_cublas();

Matrix-scalar multiplication code example:

    init_cublas();
    let out = a
        .to_device()
        .dot(&b.to_device())
        .dot(&c.to_device())
        .mul_scalar(2.0_f32)
        .to_host();
    destory_cublas();

Matrix inversion code example:

let a = array![[1.0_f32, 2.0_f32, 3.0_f32, 4.0_f32],
                [2.0_f32, 3.0_f32, 1.0_f32, 2.0_f32],
                [1.0_f32, 1.0_f32, 1.0_f32, -1.0_f32],
                [1.0_f32, 0.0_f32, -2.0_f32, -6.0_f32],
            ];
    
init_cublas();
let out = a.to_device().inv().to_host();
destory_cublas();

Using run macro can simplify the code and write it like a mathematical expression. The following is an example of using run macro.

fn least_squares_method()
{
    let x = array![[1f32, 1f32], [1f32, 2f32], [1f32, 3f32], [1f32, 4f32]];
    let y = array![[6f32], [5f32], [7f32], [10f32]];
    let bate_hat = run!(x,y => {
        let x_t = x.t();
        x_t.dot(x).inv().dot(&x_t).dot(y)
    }).to_host();
    println!("{:?}",bate_hat);
}

The example code implements the least squares method using the code that is most similar to the mathematical expression.

$$(X^TX)^{-1}X^Ty$$

V.S.

x_t.dot(x).inv().dot(&x_t).dot(y)

Safety and Error Handling

This library uses unsafe code to interface with CUDA functions. It includes error handling that checks the status of each CUDA and cuBLAS call, ensuring that any errors are handled gracefully and reported appropriately.

Performance

The performance test was conducted using the following code, comparing the dot method provided by ndarray-linalg

fn dot_with_ndarry() {
    let a = Array::from_elem((H_SIZE, H_SIZE), 1.0_f32);
    let b = Array::from_elem((H_SIZE, V_SIZE), 1.0_f32);
    let start = Instant::now();
    for _ in 0..100 {
        let _ = a.dot(&b);
    }
    println!("ndarray dot elapsed: {:.2?}", start.elapsed());
}

fn dot_with_cuda() {
    let a = Array::from_elem((H_SIZE, H_SIZE), 1.0_f32);
    let b = Array::from_elem((H_SIZE, V_SIZE), 1.0_f32);
    let start = Instant::now();
    for _ in 0..100 {
        let _ = a.cuda_dot(&b);
    }
    println!("matmul elapsed: {:.2?}", start.elapsed());
}

Comparing result:

Rows	columns	run times	ndarra-linalg	cuda_dot
64	64	100	2.27ms	9.89ms
128	80	100	11.37ms	10.66ms
768	128	100	438.01ms	57.86ms
2048	1000	100	22800ms	323.30ms

Contribution

Contributions to this library are welcome! Whether it's through reporting issues, proposing new features, improving documentation, or submitting pull requests, all forms of contribution are encouraged.

License

This library is distributed under the MIT license.