An abstraction library to run kernels on both CUDA and OpenCL.
You need to write the code that interacts with the GPU only once. Below is such code that runs a
kernel on CUDA and/or OpenCL. For a full working example, please see the examples
directory. You can run it via cargo run --example add.
let closures = program_closures!(|program, _args| -> Result<Vec<u32>, GPUError> {
// Make sure the input data has the same length.
assert_eq!(aa.len(), bb.len());
let length = aa.len();
// Copy the data to the GPU.
let aa_buffer = program.create_buffer_from_slice(&aa)?;
let bb_buffer = program.create_buffer_from_slice(&bb)?;
// The result buffer has the same length as the input buffers.
let result_buffer = unsafe { program.create_buffer::<u32>(length)? };
// Get the kernel.
let kernel = program.create_kernel("add", 8, 4)?;
// Execute the kernel.
kernel
.arg(&(length as u32))
.arg(&aa_buffer)
.arg(&bb_buffer)
.arg(&result_buffer)
.run()?;
// Get the resulting data.
let mut result = vec![0u32; length];
program.read_into_buffer(&result_buffer, &mut result)?;
Ok(result)
});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.
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.