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use std::{borrow::Cow, str::FromStr};
use wgpu::util::DeviceExt;
// Indicates a u32 overflow in an intermediate Collatz value
const OVERFLOW: u32 = 0xffffffff;
async fn run() {
let numbers = if std::env::args().len() <= 1 {
let default = vec![1, 2, 3, 4];
println!("No numbers were provided, defaulting to {:?}", default);
default
} else {
std::env::args()
.skip(1)
.map(|s| u32::from_str(&s).expect("You must pass a list of positive integers!"))
.collect()
};
let steps = execute_gpu(&numbers).await.unwrap();
let disp_steps: Vec<String> = steps
.iter()
.map(|&n| match n {
OVERFLOW => "OVERFLOW".to_string(),
_ => n.to_string(),
})
.collect();
println!("Steps: [{}]", disp_steps.join(", "));
#[cfg(target_arch = "wasm32")]
log::info!("Steps: [{}]", disp_steps.join(", "));
}
async fn execute_gpu(numbers: &[u32]) -> Option<Vec<u32>> {
// Instantiates instance of WebGPU
let instance = wgpu::Instance::new(wgpu::Backends::all());
// `request_adapter` instantiates the general connection to the GPU
let adapter = instance
.request_adapter(&wgpu::RequestAdapterOptions::default())
.await?;
// `request_device` instantiates the feature specific connection to the GPU, defining some parameters,
// `features` being the available features.
let (device, queue) = adapter
.request_device(
&wgpu::DeviceDescriptor {
label: None,
features: wgpu::Features::empty(),
limits: wgpu::Limits::downlevel_defaults(),
},
None,
)
.await
.unwrap();
let info = adapter.get_info();
// skip this on LavaPipe temporarily
if info.vendor == 0x10005 {
return None;
}
execute_gpu_inner(&device, &queue, numbers).await
}
async fn execute_gpu_inner(
device: &wgpu::Device,
queue: &wgpu::Queue,
numbers: &[u32],
) -> Option<Vec<u32>> {
// Loads the shader from WGSL
let cs_module = device.create_shader_module(&wgpu::ShaderModuleDescriptor {
label: None,
source: wgpu::ShaderSource::Wgsl(Cow::Borrowed(include_str!("shader.wgsl"))),
});
// Gets the size in bytes of the buffer.
let slice_size = numbers.len() * std::mem::size_of::<u32>();
let size = slice_size as wgpu::BufferAddress;
// Instantiates buffer without data.
// `usage` of buffer specifies how it can be used:
// `BufferUsages::MAP_READ` allows it to be read (outside the shader).
// `BufferUsages::COPY_DST` allows it to be the destination of the copy.
let staging_buffer = device.create_buffer(&wgpu::BufferDescriptor {
label: None,
size,
usage: wgpu::BufferUsages::MAP_READ | wgpu::BufferUsages::COPY_DST,
mapped_at_creation: false,
});
// Instantiates buffer with data (`numbers`).
// Usage allowing the buffer to be:
// A storage buffer (can be bound within a bind group and thus available to a shader).
// The destination of a copy.
// The source of a copy.
let storage_buffer = device.create_buffer_init(&wgpu::util::BufferInitDescriptor {
label: Some("Storage Buffer"),
contents: bytemuck::cast_slice(numbers),
usage: wgpu::BufferUsages::STORAGE
| wgpu::BufferUsages::COPY_DST
| wgpu::BufferUsages::COPY_SRC,
});
// A bind group defines how buffers are accessed by shaders.
// It is to WebGPU what a descriptor set is to Vulkan.
// `binding` here refers to the `binding` of a buffer in the shader (`layout(set = 0, binding = 0) buffer`).
// A pipeline specifies the operation of a shader
// Instantiates the pipeline.
let compute_pipeline = device.create_compute_pipeline(&wgpu::ComputePipelineDescriptor {
label: None,
layout: None,
module: &cs_module,
entry_point: "main",
});
// Instantiates the bind group, once again specifying the binding of buffers.
let bind_group_layout = compute_pipeline.get_bind_group_layout(0);
let bind_group = device.create_bind_group(&wgpu::BindGroupDescriptor {
label: None,
layout: &bind_group_layout,
entries: &[wgpu::BindGroupEntry {
binding: 0,
resource: storage_buffer.as_entire_binding(),
}],
});
// A command encoder executes one or many pipelines.
// It is to WebGPU what a command buffer is to Vulkan.
let mut encoder =
device.create_command_encoder(&wgpu::CommandEncoderDescriptor { label: None });
{
let mut cpass = encoder.begin_compute_pass(&wgpu::ComputePassDescriptor { label: None });
cpass.set_pipeline(&compute_pipeline);
cpass.set_bind_group(0, &bind_group, &[]);
cpass.insert_debug_marker("compute collatz iterations");
cpass.dispatch(numbers.len() as u32, 1, 1); // Number of cells to run, the (x,y,z) size of item being processed
}
// Sets adds copy operation to command encoder.
// Will copy data from storage buffer on GPU to staging buffer on CPU.
encoder.copy_buffer_to_buffer(&storage_buffer, 0, &staging_buffer, 0, size);
// Submits command encoder for processing
queue.submit(Some(encoder.finish()));
// Note that we're not calling `.await` here.
let buffer_slice = staging_buffer.slice(..);
// Gets the future representing when `staging_buffer` can be read from
let buffer_future = buffer_slice.map_async(wgpu::MapMode::Read);
// Poll the device in a blocking manner so that our future resolves.
// In an actual application, `device.poll(...)` should
// be called in an event loop or on another thread.
device.poll(wgpu::Maintain::Wait);
// Awaits until `buffer_future` can be read from
if let Ok(()) = buffer_future.await {
// Gets contents of buffer
let data = buffer_slice.get_mapped_range();
// Since contents are got in bytes, this converts these bytes back to u32
let result = bytemuck::cast_slice(&data).to_vec();
// With the current interface, we have to make sure all mapped views are
// dropped before we unmap the buffer.
drop(data);
staging_buffer.unmap(); // Unmaps buffer from memory
// If you are familiar with C++ these 2 lines can be thought of similarly to:
// delete myPointer;
// myPointer = NULL;
// It effectively frees the memory
// Returns data from buffer
Some(result)
} else {
panic!("failed to run compute on gpu!")
}
}
fn main() {
#[cfg(not(target_arch = "wasm32"))]
{
env_logger::init();
pollster::block_on(run());
}
#[cfg(target_arch = "wasm32")]
{
std::panic::set_hook(Box::new(console_error_panic_hook::hook));
console_log::init().expect("could not initialize logger");
wasm_bindgen_futures::spawn_local(run());
}
}
#[cfg(all(test, not(target_arch = "wasm32")))]
mod tests;