https://github.com/tmrob2/cuda2rust_sandpit

Minimal examples to get CUDA linear algebra programs working with Rust using CC & FFI.
https://github.com/tmrob2/cuda2rust_sandpit

cc clang cublas cuda cusparse rust

Last synced: about 2 months ago
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Minimal examples to get CUDA linear algebra programs working with Rust using CC & FFI.

• Host: GitHub
• URL: https://github.com/tmrob2/cuda2rust_sandpit
• Owner: tmrob2
• Created: 2022-10-20T01:04:29.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-23T03:12:33.000Z (almost 2 years ago)
• Last Synced: 2024-06-11T03:32:32.872Z (3 months ago)
• Topics: cc, clang, cublas, cuda, cusparse, rust
• Language: Rust
• Homepage:
• Size: 13.7 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-cuda-and-hpc - tmrob2/cuda2rust_sandpit

README

        
# cuda2rust_sandpit

Simplest configuration and testing of Rust sparse matrix

data structures for sparse matrix linear algebra on CUDA. 

First, define a `build.rs` which configures the linking of

cuda libs necessary for computation. Then `C` libraries are

created under `myclib` which creates the most basic interface for controlling cuBlas and cuSparse linalg routines. 

A very simple FFI is constructed in `lib.rs` which references functions in `myclib`, and `build.rs` manages the construction of the dynamic shared library using:

```Rust

cc::Build::new()

        .file("myclib/mycfuncs.c")

        .file("myclib/test_cublas.c")

        .file("myclib/test_cusparse.c")

        .file("myclib/sparse_funcs.c")

        .cuda(true)

        .compile("mycfuncs");

```

The idea is to manage memory using Rust, bue still leverage the low level power of CUDA which is most conveniently accessed via a custom `C` library. 

## Why not use `cuBlas` FFI?

I don't see any point in using a monolithic FFI for CUDA libs when it is clear that CUDA interfaces will not be natively written in `Rust` any time soon. Further, there is no FFI for `cuSparse` which is arguably the more important CUDA library. 

It is much easier to write a small implementation using the `cuSparse` header and then create a small FFI related to a `Rust` project, which is playing to the strengths of both languages.

## Implementation Notes

A quick note on `build.rs` linking ordering. For `cargo test` to work the linked libraries need to go after `cc::Build::new()` otherwise a linking error will be generated, i.e.:

```Rust

cc::Build::new()

    .file("myclib/mycfuncs.c")

    .file("myclib/test_cublas.c")

    .file("myclib/test_cusparse.c")

    .cuda(true)

    .compile("mycfuncs");

    

println!("cargo:rustc-link-lib=dylib=cublas");

```

## Tests

Run lib tests with `cargo test`.