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gpgpu

GitHub Workflow Status (branch) Crates.io Crates.io docs.rs

An experimental async GPU compute library based on wgpu. It is meant to be used alongside wgpu if desired.

To start using gpgpu, just create a Framework instance and follow the examples in the main repository.

Example

Small program that multiplies 2 vectors A and B; and stores the result in another vector C.

Rust program

    use gpgpu::*;
fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Framework initialization
    let fw = Framework::default();

    // Original CPU data
    let cpu_data = (0..10000).into_iter().collect::<Vec<u32>>();

    // GPU buffer creation
    let buf_a = GpuBuffer::from_slice(&fw, &cpu_data);       // Input
    let buf_b = GpuBuffer::from_slice(&fw, &cpu_data);       // Input
    let buf_c = GpuBuffer::<u32>::with_capacity(&fw, cpu_data.len() as u64);  // Output

    // Shader load from SPIR-V binary file
    let shader = Shader::from_spirv_file(&fw, "<SPIR-V shader path>")?;
    //  or from a WGSL source file
    let shader = Shader::from_wgsl_file(&fw, "<WGSL shader path>")?;    

    // Descriptor set and program creation
    let desc = DescriptorSet::default()
        .bind_buffer(&buf_a, GpuBufferUsage::ReadOnly)
        .bind_buffer(&buf_b, GpuBufferUsage::ReadOnly)
        .bind_buffer(&buf_c, GpuBufferUsage::ReadWrite);
    let program = Program::new(&shader, "main").add_descriptor_set(desc); // Entry point

    // Kernel creation and enqueuing
    Kernel::new(&fw, program).enqueue(cpu_data.len() as u32, 1, 1); // Enqueuing, not very optimus 😅

    let output = buf_c.read_vec_blocking()?;                        // Read back C from GPU
    for (a, b) in cpu_data.into_iter().zip(output) {
        assert_eq!(a.pow(2), b);
    }

    Ok(())
}

Shader program

The shader is written in WGSL

// Vector type definition. Used for both input and output
struct Vector {
    data: array<u32>,
}

// A, B and C vectors
@group(0) @binding(0) var<storage, read>  a: Vector;
@group(0) @binding(1) var<storage, read>  b: Vector;
@group(0) @binding(2) var<storage, read_write> c: Vector;

@compute @workgroup_size(1)
fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
    c.data[global_id.x] = a.data[global_id.x] * b.data[global_id.x];
}