Struct rust_gpu_tools::cuda::Program

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pub struct Program { /* private fields */ }

Expand description

Abstraction that contains everything to run a CUDA kernel on a GPU.

The majority of methods are the same as crate::opencl::Program, so you can write code using this API, which will then work with OpenCL as well as CUDA kernels.

Implementations

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impl Program

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pub fn device_name(&self) -> &str

Returns the name of the GPU, e.g. “GeForce RTX 3090”.

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pub fn from_binary(
device: &Device,
filename: &CStr
) -> Result<Program, GPUError>

Creates a program for a specific device from a compiled CUDA binary file.

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pub fn from_bytes(device: &Device, bytes: &[u8]) -> Result<Program, GPUError>

Creates a program for a specific device from a compiled CUDA binary.

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pub unsafe fn create_buffer<T>(
&self,
length: usize
) -> Result<Buffer<T>, GPUError>

Creates a new buffer that can be used for input/output with the GPU.

The length is the number of elements to create.

It is usually used to create buffers that are initialized by the GPU. If you want to directly transfer data from the host to the GPU, you would use the safe Program::create_buffer_from_slice instead.

Safety

The buffer needs to be initalized (by the host with Program::write_from_buffer) or by the GPU) before it can be read via Program::read_into_buffer.

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pub fn create_buffer_from_slice<T>(
&self,
slice: &[T]
) -> Result<Buffer<T>, GPUError>

Creates a new buffer on the GPU and initializes with the given slice.

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pub fn create_kernel(
 &self,
 name: &str,
 gws: usize,
 lws: usize
) -> Result<Kernel<'_>, GPUError>

Returns a kernel.

The global_work_size does not follow the OpenCL definition. It is not the total number of threads. Instead it follows CUDA’s definition and is the number of local_work_size sized thread groups. So the total number of threads is global_work_size * local_work_size.

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pub fn write_from_buffer<T>(
 &self,
 buffer: &mut Buffer<T>,
 data: &[T]
) -> Result<(), GPUError>

Puts data from an existing buffer onto the GPU.

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pub fn read_into_buffer<T>(
 &self,
 buffer: &Buffer<T>,
 data: &mut [T]
) -> Result<(), GPUError>

Reads data from the GPU into an existing buffer.

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pub fn run<F, R, E, A>(&self, fun: F, arg: A) -> Result<R, E> where
F: FnOnce(&Self, A) -> Result<R, E>,
E: From<GPUError>,

Run some code in the context of the program.

It sets the correct contexts.

It takes the program as a parameter, so that we can use the same function body, for both the OpenCL and the CUDA code path. The only difference is the type of the program.