Asynchronous CUDA, NPP and TensorRT

ℹ️ Introduction

The async-cuda family of libraries is an experimental set of libraries for interacting with the GPU asynchronously. Since the GPU is just another I/O device (from the point of view of your program), the async model actually fits surprisingly well.

The way it is implemented in async-cuda is that all operations are scheduled on a single runtime thread that drives the GPU. The interface of this library enforces that synchronization happens when it is necessary (and synchronization itself is also asynchronous).

The async-cuda project consists of:

• async-cuda-core: CUDA core primitives such as streams and buffers.
• async-cuda-npp: Common NPP operations such as resizing and cropping.
• async-tensorrt: Minimal wrapper for TensorRT.

🛠 S️️tatus

This project is still a work-in-progress, and will contain bugs. Some parts of the API have not been flushed out yet. Use with caution.

⚠️ Safety warning

The async-cuda crates are intentionally unsafe. Due to the limitations of how async Rust currently works, usage of the async interface of this crate can cause undefined behavior in some rare cases. It is up to the user of this crate to prevent this from happening by following these rules:

• No futures produced by functions in this crate may be leaked (either by std::mem::forget or otherwise).
• Use a well-behaved runtime (one that will not forget your future) like Tokio or async-std.

Internally, the Future type in this crate schedules a CUDA call on a separate runtime thread. To make the API as ergonomic as possible, the lifetime bounds of the closure (that is sent to the runtime) are tied to the future object. To enforce this bound, the future will block and wait if it is dropped. This mechanism relies on the future being driven to completion, and not forgotten. This is not necessarily guaranteed. Unsafety may arise if either the runtime gives up on or forgets the future, or the caller manually polls the future, then forgets it.

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.