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use std::ops::Div;
use nalgebra::{DVector, Vector4};
use wgcore::composer::ComposerExt;
use wgcore::gpu::GpuInstance;
use wgcore::kernel::{KernelInvocationBuilder, KernelInvocationQueue};
use wgcore::tensor::{GpuScalar, GpuVector};
use wgcore::Shader;
use wgpu::{BufferUsages, ComputePipeline};
use wgcore::hot_reloading::HotReloadState;
use wgcore::timestamps::GpuTimestamps;
#[derive(Shader)]
#[shader(
src = "timestamp_queries.wgsl",
composable = false
)]
struct ShaderTimestampQueries {
main: ComputePipeline
}
#[async_std::main]
async fn main() -> anyhow::Result<()> {
// Initialize the gpu device and its queue.
//
// Note that `GpuInstance` is just a simple helper struct for initializing the gpu resources.
// You are free to initialize them independently if more control is needed, or reuse the ones
// that were already created/owned by e.g., a game engine.
let gpu = GpuInstance::new().await?;
// Load and compile our kernel. The `from_device` function was generated by the `Shader` derive.
// Note that its dependency to `Composable` is automatically resolved by the `Shader` derive
// too.
let mut kernel = ShaderTimestampQueries::from_device(gpu.device())?;
// Create the buffers.
const LEN: u32 = 2_000_000;
let buffer = GpuVector::init(gpu.device(), vec![0u32; LEN as usize], BufferUsages::STORAGE | BufferUsages::COPY_SRC);
// Init hot-reloading.
// We are setting up hot-reloading so that we can change somme elements in the shader
// (like the iteration count) and see how that affects performances live.
let mut hot_reload = HotReloadState::new()?;
ShaderTimestampQueries::watch_sources(&mut hot_reload)?;
// Init timestamp queries.
// To measure the time of one kernel, we need two timestamps (one for when it starts and one for
// when it stopped).
let mut timestamps = GpuTimestamps::new(gpu.device(), 2);
// Queue the operation.
println!("#############################");
println!("Edit the file `timestamp_queries.wgsl` (for example by multiplying or dividing NUM_ITERS by 10).\nThe updated runtime will be printed below whenever a change is detected.");
println!("#############################");
for loop_id in 0.. {
// Detect & apply changes.
hot_reload.update_changes();
match kernel.reload_if_changed(gpu.device(), &hot_reload) {
Ok(changed) => {
if changed {
// Clear the timestamps to reuse in the next loop.
timestamps.clear();
// We detected a change (or this is the first loop).
// Read the result.
let mut queue = KernelInvocationQueue::new(gpu.device());
// Declare a compute pass with timestamps enabled.
queue.compute_pass("timestamp_queries_test", true);
KernelInvocationBuilder::new(&mut queue, &kernel.main)
.bind0([buffer.buffer()])
.queue(LEN.div_ceil(64));
// Encode & submit the operation to the gpu.
let mut encoder = gpu.device().create_command_encoder(&Default::default());
// Run our kernel.
queue.encode(&mut encoder, Some(&mut timestamps));
// Resolve the timestamp queries.
timestamps.resolve(&mut encoder);
gpu.queue().submit(Some(encoder.finish()));
// Read and print the kernel’s runtime.
let timestamps_read = timestamps.wait_for_results_ms(gpu.device(), gpu.queue());
println!("Current run time: {}ms", timestamps_read[1] - timestamps_read[0]);
}
}
Err(e) => {
// Hot-reloading failed, likely due to a syntax error in the shader.
println!("Hot reloading error: {:?}", e);
}
}
}
Ok(())
}